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ORCIDBriefings in Bioinformatics, Volume 25
Volume 25, Number 1, November 2023
- Junjie Zhu
, Zhengxin Li, Haowei Tong, Zhouyu Lu, Ningjie Zhang, Ting Wei, Hai-Feng Chen:
Phanto-IDP: compact model for precise intrinsically disordered protein backbone generation and enhanced sampling. - Dong Liu, Biao Zhang, Jun Liu, Hui Li, Le Song, Guijun Zhang:
Assessing protein model quality based on deep graph coupled networks using protein language model. - Milos Musil, Andrej Jezik, Jana Horackova, Simeon Borko, Petr Kabourek, Jirí Damborský, David Bednar:
FireProt 2.0: web-based platform for the fully automated design of thermostable proteins. - Francis Yew Fu Tieng, Muhammad Redha Abdullah Zawawi, Nur Alyaa Afifah Md Shahri, Zeti-Azura Mohamed-Hussein, Learn-Han Lee, Nurul-Syakima Ab Mutalib:
A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools. - Jiawei Zhang, Wang Ma, Hui Yao:
Accurate TCR-pMHC interaction prediction using a BERT-based transfer learning method. - Miles McGibbon, Steven R. Shave, Jie Dong, Yumiao Gao, Douglas R. Houston, Jiancong Xie, Yuedong Yang, Philippe Schwaller, Vincent Blay:
From intuition to AI: evolution of small molecule representations in drug discovery. - Nhat Truong Pham, Le Thi Phan, Jimin Seo, Yeonwoo Kim, Minkyung Song, Sukchan Lee, Young-Jun Jeon, Balachandran Manavalan:
Advancing the accuracy of SARS-CoV-2 phosphorylation site detection via meta-learning approach. - Wenxuan Xing, Jie Zhang, Chen Li, Yujia Huo, Gaifang Dong:
iAMP-Attenpred: a novel antimicrobial peptide predictor based on BERT feature extraction method and CNN-BiLSTM-Attention combination model. - Qingyang Yin, Liang Chen:
CellTICS: an explainable neural network for cell-type identification and interpretation based on single-cell RNA-seq data. - Zikun Yang, Basilio Cieza, Dolly Reyes-Dumeyer, Rosa Montesinos, Marcio Soto-Añari, Nilton Custodio, Giuseppe Tosto:
A benchmark study on current GWAS models in admixed populations. - Yajie Meng, Yi Wang, Junlin Xu, Changcheng Lu, Xianfang Tang, Tao Peng, Ben-gong Zhang, Geng Tian, Jialiang Yang:
Drug repositioning based on weighted local information augmented graph neural network. - Elena Lucy Carter, Chrystala Constantinidou, Mohammad Tauqeer Alam:
Applications of genome-scale metabolic models to investigate microbial metabolic adaptations in response to genetic or environmental perturbations. - Ping Fu, Yifan Wu, Zhiyuan Zhang, Ye Qiu, Yirong Wang, Yousong Peng:
VIGA: a one-stop tool for eukaryotic virus identification and genome assembly from next-generation-sequencing data. - Yu Mei Wang, Yuzhi Sun, Beiying Wang, Zhiping Wu, Xiao-Ying He, Yuansong Zhao:
Transfer learning for clustering single-cell RNA-seq data crossing-species and batch, case on uterine fibroids. - Peilong Li, Junfeng Wei, Ying Zhu:
CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation. - Correction to: Breaking the barriers of data scarcity in drug-target affinity prediction.
- Daniel Toro-Domínguez, Jordi Martorell-Marugan, Manuel Martínez-Bueno, Raúl López-Domínguez, Elena Carnero-Montoro, Guillermo Barturen, Daniel Goldman, Michelle Petri, Pedro Carmona-Saez, Marta E. Alarcón-Riquelme:
Response to the letter 'testing the effectiveness of MyPROSLE in classifying patients with lupus nephritis'. - Sang-Ho Yoon, Jin-Wu Nam:
Clustering malignant cell states using universally variable genes. - Jiaxian Yan, Zhaofeng Ye, Ziyi Yang, Chengqiang Lu, Shengyu Zhang, Qi Liu, Jiezhong Qiu:
Multi-task bioassay pre-training for protein-ligand binding affinity prediction. - Pavel Akhtyamov, Layal Shaheen, Mikhail Raevskiy, Alexey Stupnikov, Yulia A. Medvedeva:
scATAC-seq preprocessing and imputation evaluation system for visualization, clustering and digital footprinting. - Jie Dong, Zheng Wu, Huanle Xu, Defang Ouyang:
FormulationAI: a novel web-based platform for drug formulation design driven by artificial intelligence. - So-Ra Han, Mingyu Park, Sai Kosaraju, Jeungmin Lee, Hyun Lee, Jun Hyuck Lee, Tae-Jin Oh, Mingon Kang:
Evidential deep learning for trustworthy prediction of enzyme commission number. - Jorge Beltrán, Lisandra Herrera Belén, Jorge G. Farias, Mauricio Zamorano, Nicolás Lefin, Javiera Miranda, Fernanda Parraguez Contreras:
VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation. - Cuiling Wu, Yiyi Zhang, Zhiwen Ying, Ling Li, Jun Wang, Hui Yu, Mengchen Zhang, Xianzhong Feng, Xinghua Wei, Xiaogang Xu:
A transformer-based genomic prediction method fused with knowledge-guided module. - Min Zou, Honghao Li, Dongqing Su, Yuqiang Xiong, Haodong Wei, Shiyuan Wang, Hongmei Sun, Tao Wang, Qilemuge Xi, Yongchun Zuo, Lei Yang:
Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes. - Hao Qian, Wenjing Huang, Shikui Tu, Lei Xu:
KGDiff: towards explainable target-aware molecule generation with knowledge guidance. - Jan Stourac, Simeon Borko, Rayyan T. Khan, Petra Pokorna, Adam Dobias, Joan Planas-Iglesias, Stanislav Mazurenko, Gaspar R. P. Pinto, Veronika Szotkowska, Jaroslav Sterba, Ondrej Slaby, Jirí Damborský, David Bednar:
PredictONCO: a web tool supporting decision-making in precision oncology by extending the bioinformatics predictions with advanced computing and machine learning. - Lei Chen, Yuwei Chen:
RMTLysPTM: recognizing multiple types of lysine PTM sites by deep analysis on sequences. - Zhen-Ning Yin, Fei-Liao Lai, Feng Gao:
Unveiling human origins of replication using deep learning: accurate prediction and comprehensive analysis. - Qiyiwen Zhang, Changgee Chang, Qi Long:
Robust knowledge-guided biclustering for multi-omics data. - Ruofan Ding, Xudong Zou, Yangmei Qin, Lihai Gong, Hui Chen, Xuelian Ma, Shouhong Guang, Chen Yu, Gao Wang, Lei Li:
xQTLbiolinks: a comprehensive and scalable tool for integrative analysis of molecular QTLs. - Qisheng Pan, Stephanie Portelli, Thanh-Binh Nguyen, David B. Ascher:
Characterization on the oncogenic effect of the missense mutations of p53 via machine learning. - Stavros Makrodimitris, Bram Pronk, Tamim Abdelaal, Marcel J. T. Reinders:
An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics. - Shuheng Pan, Xinyi Jiang, Kai Zhang:
WSGMB: weight signed graph neural network for microbial biomarker identification. - Qilin Wang, Junyou Zhang, Zhaoshuo Liu, Yingying Duan, Chunyan Li:
Integrative approaches based on genomic techniques in the functional studies on enhancers. - Fanding Xu, Zhiwei Yang, Lizhuo Wang, Deyu Meng, Jiangang Long:
MESPool: Molecular Edge Shrinkage Pooling for hierarchical molecular representation learning and property prediction. - Juntao Li, Hongmei Zhang, Bingyu Mu, Hongliang Zuo, Kanglei Zhou:
Identifying phenotype-associated subpopulations through LP_SGL. - Jiahao Zheng, Yuedong Yang, Zhiming Dai:
Subgraph extraction and graph representation learning for single cell Hi-C imputation and clustering. - Zhen Wang, Zheng Feng, Yanjun Li, Bowen Li, Yongrui Wang, Chulin Sha, Min He, Xiaolin Li:
BatmanNet: bi-branch masked graph transformer autoencoder for molecular representation. - Benzhe Su, Weiwei Wang, Xiaohui Lin, Shenglan Liu, Xin Huang:
Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases. - Kevin Tippenhauer, Marwin Philips, Carlo R. Largiadèr, Murat Sariyar:
Using the PharmCAT tool for Pharmacogenetic clinical decision support.
Volume 25, Number 2, January 2024
- Rajesh Kumar Pathak, Jun-Mo Kim:
Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare. - Fengjuan Huang, Xinjie Fan, Ying Wang, Yu Zou, Jiangfang Lian, Chuang Wang, Feng Ding, Yunxiang Sun:
Computational insights into the cross-talk between medin and Aβ: implications for age-related vascular risk factors in Alzheimer's disease. - Sheng-Yong Xu, Shanshan Cai, Zhi-Qiang Han:
Evaluating the efficacy of MEANGS for mitochondrial genome assembly of cartilaginous and ray-finned fish species. - Minghao Fang, Jingwen Fang, Songwen Luo, Ke Liu, Qiaoni Yu, Jiaxuan Yang, Youyang Zhou, Zongkai Li, Ruoming Sun, Chuang Guo, Kun Qu:
eccDNA-pipe: an integrated pipeline for identification, analysis and visualization of extrachromosomal circular DNA from high-throughput sequencing data. - Yi-Heng Zhu, Zi Liu, Yan Liu, Zhiwei Ji, Dong-Jun Yu:
ULDNA: integrating unsupervised multi-source language models with LSTM-attention network for high-accuracy protein-DNA binding site prediction. - Hangjia Zhao, Michael Baudis:
labelSeg: segment annotation for tumor copy number alteration profiles. - Simone Maestri, Mattia Furlan, Logan Mulroney, Lucia Coscujuela Tarrero, Camilla Ugolini, Fabio Dalla Pozza, Tommaso Leonardi, Ewan Birney, Francesco Nicassio, Mattia Pelizzola:
Benchmarking of computational methods for m6A profiling with Nanopore direct RNA sequencing. - Ariadna Llop-Peiró, Gerard Pujadas, Santiago Garcia-Vallvé:
Challenges in distinguishing functional proteins from polyproteins in databases: implications for drug discovery. - Yuyao Zhai, Liang Chen, Minghua Deng:
scEVOLVE: cell-type incremental annotation without forgetting for single-cell RNA-seq data. - Sowmya Ramaswamy Krishnan, Arijit Roy, M. Michael Gromiha:
Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning. - Wang Xu, Houfang Zhang, Wenhan Guo, Lijun Jiang, Yunjie Zhao, Yunhui Peng:
Deciphering principles of nucleosome interactions and impact of cancer-associated mutations from comprehensive interaction network analysis. - Xinyu Guo, Liang Chen:
From G1 to M: a comparative study of methods for identifying cell cycle phases. - Musu Yuan, Hui Wan, Zihao Wang, Qirui Guo, Minghua Deng:
SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data. - Mengting Niu, Chunyu Wang, Yaojia Chen, Quan Zou, Lei Xu:
Identification, characterization and expression analysis of circRNA encoded by SARS-CoV-1 and SARS-CoV-2. - Qiuchen Meng, Lei Wei, Kun Ma, Ming Shi, Xinyi Lin, Joshua W. K. Ho, Yinqing Li, Xuegong Zhang:
scDecouple: decoupling cellular response from infected proportion bias in scCRISPR-seq. - Songqi Zhou, Yang Li, Wenyuan Wu, Li Li:
scMMT: a multi-use deep learning approach for cell annotation, protein prediction and embedding in single-cell RNA-seq data. - Mona Nourbakhsh, Kristine Degn, Astrid Saksager, Matteo Tiberti, Elena Papaleo:
Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks. - Jiaxuan Liu, Yonggang Lu, Li Zhu:
A kinetic model for solving a combination optimization problem in ab-initio Cryo-EM 3D reconstruction. - Yury A Barbitoff, Mikhail O. Ushakov, Tatyana E. Lazareva, Yulia A Nasykhova, Andrey S. Glotov, Alexander V. Predeus:
Bioinformatics of germline variant discovery for rare disease diagnostics: current approaches and remaining challenges. - Yaofeng Hu, Kai Xiao, Hengyu Yang, Xiaoping Liu, Chuanchao Zhang, Qianqian Shi:
Spatially contrastive variational autoencoder for deciphering tissue heterogeneity from spatially resolved transcriptomics. - Lu-Xiang Guo, Lei Wang, Zhu-Hong You, Chang-Qing Yu, Meng-Lei Hu, Bo-Wei Zhao, Yang Li:
Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations. - Junxiang Zeng, Xiupan Gao, Limei Gao, Youyou Yu, Lisong Shen, Xiujun Pan:
Recognition of rare antinuclear antibody patterns based on a novel attention-based enhancement framework. - Merve Vural-Ozdeniz, Kubra Calisir, Rana Acar, Aysenur Yavuz, Mustafa M. Ozgur, Ertugrul Dalgic, Ozlen Konu:
CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters. - Lijuan Wang, Ying Lu, Doudou Li, Yajing Zhou, Lili Yu, Ines Mesa Eguiagaray, Harry Campbell, Xue Li, Evropi Theodoratou:
The landscape of the methodology in drug repurposing using human genomic data: a systematic review. - Min Sun, Jing Chen, Chang Zhao, Lihua Zhang, Maili Liu, Yukui Zhang, Qun Zhao, Zhou Gong:
Enhancing protein dynamics analysis with hydrophilic polyethylene glycol cross-linkers. - Jing Meng, Jingze Liu, Wenkai Song, Honglei Li, Jiangyuan Wang, Le Zhang, Yousong Peng, Aiping Wu, Taijiao Jiang:
PREDAC-CNN: predicting antigenic clusters of seasonal influenza A viruses with convolutional neural network. - Gulam Sarwar Chuwdhury, Yunshan Guo, Chi-Leung Chiang, Ka-On Lam, Ngar-Woon Kam, Zhonghua Liu, Wei Dai:
ImmuneMirror: A machine learning-based integrative pipeline and web server for neoantigen prediction. - Yue Guo, Haitao Hu, Wenbo Chen, Hao Yin, Jian Wu, Chang-Yu Hsieh, Qiaojun He, Ji Cao:
SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction. - Giovanni Visonà, Emmanuelle Bouzigon, Florence Demenais, Gabriele Beate Schweikert:
Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery. - Céline Brouard, Raphaël Mourad, Nathalie Vialaneix:
Should we really use graph neural networks for transcriptomic prediction? - Chengcheng Zhang, Tianyi Zang, Tianyi Zhao:
KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery. - Jici Jiang, Hongdi Pei, Jiayu Li, Mingxin Li, Quan Zou, Zhibin Lv:
FEOpti-ACVP: identification of novel anti-coronavirus peptide sequences based on feature engineering and optimization. - Zhen-Hao Guo, Yan-Bin Wang, Siguo Wang, Qinhu Zhang, De-Shuang Huang:
scCorrector: a robust method for integrating multi-study single-cell data. - Youngjune Park, Nils P. Muttray, Anne-Christin Hauschild:
Species-agnostic transfer learning for cross-species transcriptomics data integration without gene orthology. - Olivier Cinquin:
ChIP-GPT: a managed large language model for robust data extraction from biomedical database records. - Yibo Zhang, Wenyu Liu, Junbo Duan:
On the core segmentation algorithms of copy number variation detection tools. - Yan Miao, Zhenyuan Sun, Chenjing Ma, Chen Lin, Guohua Wang, Chunxue Yang:
VirGrapher: a graph-based viral identifier for long sequences from metagenomes. - Qingyue Wei, Md Tauhidul Islam, Yuyin Zhou, Lei Xing:
Self-supervised deep learning of gene-gene interactions for improved gene expression recovery. - Aleksandra E. Badaczewska-Dawid, Karol Wroblewski, Mateusz Kurcinski, Sebastian Kmiecik:
Structure prediction of linear and cyclic peptides using CABS-flex. - Lingyan Zheng, Shuiyang Shi, Xiuna Sun, Mingkun Lu, Yang Liao, Sisi Zhu, Hongning Zhang, Ziqi Pan, Pan Fang, Zhenyu Zeng, Honglin Li, Zhaorong Li, Weiwei Xue, Feng Zhu:
MoDAFold: a strategy for predicting the structure of missense mutant protein based on AlphaFold2 and molecular dynamics. - Seokjin Han, Ji Eun Lee, Seolhee Kang, Minyoung So, Hee Jin, Jang Ho Lee, Sunghyeob Baek, Hyungjin Jun, Tae Yong Kim, Yun-Sil Lee:
Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis. - Jin Zhang, Zikang Ma, Yan Yang, Lei Guo, Lei Du:
Modeling genotype-protein interaction and correlation for Alzheimer's disease: a multi-omics imaging genetics study. - Shuang Gu, Chaoliang Wen, Zhen Xiao, Qiang Huang, Zheyi Jiang, Honghong Liu, Jia Gao, Junying Li, Congjiao Sun, Ning Yang:
MyoV: a deep learning-based tool for the automated quantification of muscle fibers. - Fabio N. de Mello, Ana C. Tahira, Maria Gabriela Berzoti-Coelho, Sergio Verjovski-Almeida:
The CUT&RUN greenlist: genomic regions of consistent noise are effective normalizing factors for quantitative epigenome mapping. - Yue Zang, Xia Ran, Jie Yuan, Hao Wu, Youya Wang, He Li, Huajing Teng, Zhongsheng Sun:
Genomic hallmarks and therapeutic targets of ribosome biogenesis in cancer. - Zeyu Luo, Rui Wang, Yawen Sun, Junhao Liu, Herman Z. Q. Chen, Yu-Juan Zhang:
Interpretable feature extraction and dimensionality reduction in ESM2 for protein localization prediction. - Shi Zhang, Rui Zhang, Kai Yuan, Lu Yang, Chang Liu, Yuting Liu, Xumin Ni, Shuhua Xu:
Reconstructing complex admixture history using a hierarchical model. - Shuo Li, Yan Liu, Long-Chen Shen, He Yan, Jiangning Song, Dong-Jun Yu:
GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference. - Albert Katchborian-Neto, Matheus F. Alves, Paula C. P. Bueno, Karen de Jesus Nicácio, Miller S. Ferreira, Tiago B. Oliveira, Henrique Barbosa, Michael Murgu, Ana C. C. de Paula Ladvocat, Danielle F. Dias, Marisi G. Soares, João Henrique Ghilardi Lago, Daniela A Chagas-Paula:
Integrative open workflow for confident annotation and molecular networking of metabolomics MSE/DIA data. - Xiaodi Yang, Stefan Wuchty, Zeyin Liang, Li Ji, Bingjie Wang, Jialin Zhu, Ziding Zhang, Yujun Dong:
Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM. - Kandarp Joshi, Dan O. Wang:
epidecodeR: a functional exploration tool for epigenetic and epitranscriptomic regulation. - Yantong Cai, Jia Lv, Rui Li, Xiaowen Huang, Shi Wang, Zhenmin Bao, Qifan Zeng:
Deqformer: high-definition and scalable deep learning probe design method. - Jingxuan Qiu, Wanchun Nie, Hao Ding, Jia Dai, Yiwen Wei, Dezhi Li, Yuxi Zhang, Junting Xie, Xinxin Tian, Nannan Wu, Tianyi Qiu:
PB-LKS: a python package for predicting phage-bacteria interaction through local K-mer strategy. - Wenyuan Ma, Hui Wu, Yiran Chen, Hongxia Xu, Junjie Jiang, Bang Du, Mingyu Wan, Xiaolu Ma, Xiaoyu Chen, Lili Lin, Xinhui Su, Xuanwen Bao, Yifei Shen, Nong Xu, Jian Ruan, Haiping Jiang, Yongfeng Ding:
New techniques to identify the tissue of origin for cancer of unknown primary in the era of precision medicine: progress and challenges. - Tingpeng Yang, Tianze Ling, Boyan Sun, Zhendong Liang, Fan Xu, Xiansong Huang, Linhai Xie, Yonghong He, Leyuan Li, Fuchu He, Yu Wang, Cheng Chang:
Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.
Volume 25, Number 3, 2024
- Hui Wang, Dong Liu, Kailong Zhao, Yajun Wang, Guijun Zhang:
SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition. - Pengyong Li, Zhengxiang Jiang, Tianxiao Liu, Xinyu Liu, Hui Qiao, Xiaojun Yao:
Improving drug response prediction via integrating gene relationships with deep learning. - Haoquan Liu, Yunjie Zhao:
Integrated modeling of protein and RNA. - Zhenyu Wei, Chengkui Zhao, Min Zhang, Jiayu Xu, Nan Xu, Shiwei Wu, Xiaohui Xin, Lei Yu, Weixing Feng:
Meta-DHGNN: method for CRS-related cytokines analysis in CAR-T therapy based on meta-learning directed heterogeneous graph neural network. - Junxi Mu, Zhengxin Li, Bo Zhang, Qi Zhang, Jamshed Iqbal, Abdul Wadood, Ting Wei, Yan Feng, Hai-Feng Chen:
Graphormer supervised de novo protein design method and function validation. - Seunghyun Wang, Doheon Lee:
Community cohesion looseness in gene networks reveals individualized drug targets and resistance. - Zhen Gao, Yansen Su, Junfeng Xia, Rui-Fen Cao, Yun Ding, Chun-Hou Zheng, Pi-Jing Wei:
DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding. - Xinyan Wang, Kuo Yang, Ting Jia, Fanghui Gu, Chongyu Wang, Kuan Xu, Zixin Shu, Jianan Xia, Qiang Zhu, Xuezhong Zhou:
KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition. - Akram Vasighizaker, Sheena Hora, Raymond Zeng, Luis Rueda:
SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication. - Zhen Tian, Chenguang Han, Lewen Xu, Zhixia Teng, Wei Song:
MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy. - Priyotosh Sil, Ajay Subbaroyan, Saumitra Kulkarni, Olivier C. Martin, Areejit Samal:
Biologically meaningful regulatory logic enhances the convergence rate in Boolean networks and bushiness of their state transition graph. - Correction to: Introducing π-HelixNovo for practical large-scale de novo peptide sequencing.
- Chen Peng, Qiong Chen, Shangjin Tan, Xiaotao Shen, Chao Jiang:
Generalized reporter score-based enrichment analysis for omics data. - Zhao Peng, Jiaqiang Li, Xingpeng Jiang, Cuihong Wan:
sOCP: a framework predicting smORF coding potential based on TIS and in-frame features and effectively applied in the human genome. - Takashi Amisaki:
Multilevel superposition for deciphering the conformational variability of protein ensembles. - Huanhuan Liu, Qinwei Chen, Jintao Guo, Ying Zhou, Zhiyu You, Jun Ren, Yuanyuan Zeng, Jing Yang, Jialiang Huang, Qiyuan Li:
Epigenome-augmented eQTL-hotspots reveal genome-wide transcriptional programs in 36 human tissues. - Wang Yin, You Wan, Yuan Zhou:
SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding. - Xiao-Hong Ding, Yi Xiao, Fenfang Chen, Cheng-Lin Liu, Tong Fu, Zhi-Ming Shao, Yi-Zhou Jiang:
The HLA-I landscape confers prognosis and antitumor immunity in breast cancer. - Boya Ji, Haitao Zou, Liwen Xu, Xiaolan Xie, Shaoliang Peng:
MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA-disease associations prediction. - Thomas G. Brooks, Nicholas F. Lahens, Antonijo Mrcela, Dimitra Sarantopoulou, Soumyashant Nayak, Amruta Naik, Shaon Sengupta, Peter S. Choi, Gregory R. Grant:
BEERS2: RNA-Seq simulation through high fidelity in silico modeling. - Ken Chen, Yue Zhou, Maolin Ding, Yu Wang, Zhixiang Ren, Yuedong Yang:
Self-supervised learning on millions of primary RNA sequences from 72 vertebrates improves sequence-based RNA splicing prediction. - Tram Huynh, Zixuan Cang:
Topological and geometric analysis of cell states in single-cell transcriptomic data. - Guoxuan Ma, Jian Kang, Tianwei Yu:
Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes. - Qiuhao Chen, Liyuan Zhang, Yaojia Liu, Zhonghao Qin, Tianyi Zhao:
PUTransGCN: identification of piRNA-disease associations based on attention encoding graph convolutional network and positive unlabelled learning. - Correction to: Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics.
- Zechen Wang, Sheng Wang, Yangyang Li, Jingjing Guo, Yanjie Wei, Yuguang Mu, Liangzhen Zheng, Weifeng Li:
A new paradigm for applying deep learning to protein-ligand interaction prediction. - Marc-Antoine Gerault, Samuel Granjeaud, Luc Camoin, Pär Nordlund, Lingyun Dai:
IMPRINTS.CETSA and IMPRINTS.CETSA.app: an R package and a Shiny application for the analysis and interpretation of IMPRINTS-CETSA data. - Kongming Li, Jiahao Li, Yuhao Tao, Fei Wang:
stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics. - Le Zhang, Wenkai Song, Tinghao Zhu, Yang Liu, Wei Chen, Yang Cao:
ConvNeXt-MHC: improving MHC-peptide affinity prediction by structure-derived degenerate coding and the ConvNeXt model. - Hongyuan Zhao, Suyi Zhang, Hui Qin, Xiaogang Liu, Dongna Ma, Xiao Han, Jian Mao, Shuangping Liu:
DSNetax: a deep learning species annotation method based on a deep-shallow parallel framework. - Tinghe Zhang, Sumin Jo, Michelle Zhang, Kai Wang, Shou-Jiang Gao, Yufei Huang:
Understanding YTHDF2-mediated mRNA degradation by m6A-BERT-Deg. - Weihang Zhang, Yang Cui, Bowen Liu, Martin Loza, Sung-Joon Park, Kenta Nakai:
HyGAnno: hybrid graph neural network-based cell type annotation for single-cell ATAC sequencing data. - Theinmozhi Arulraj, Hanwen Wang, Alberto Ippolito, Shuming Zhang, Elana J. Fertig, Aleksander S. Popel:
Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology. - Ming-Siang Huang, Jen-Chieh Han, Pei-Yen Lin, Yu-Ting You, Richard Tzong-Han Tsai, Wen-Lian Hsu:
Surveying biomedical relation extraction: a critical examination of current datasets and the proposal of a new resource. - Meng Wang, Chuqi Lei, Jianxin Wang, Yaohang Li, Min Li:
TripHLApan: predicting HLA molecules binding peptides based on triple coding matrix and transfer learning. - Danel Olaverri-Mendizabal, Luis Vitores Valcarcel, Naroa Barrena, Carlos J. Rodríguez, Francisco J. Planes:
Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism. - Correction to: Inflated false discovery rate due to volcano plots: problem and solutions.
- Lixin Lei, Kaitai Han, Zijun Wang, Chaojing Shi, Zhenghui Wang, Ruoyan Dai, Zhiwei Zhang, Mengqiu Wang, Qianjin Guo:
Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics. - Yue Kang, Hongyu Zhang, Jinting Guan:
scINRB: single-cell gene expression imputation with network regularization and bulk RNA-seq data. - Jinze Huang, Yang Zhao, Bo Meng, Ao Lu, Yaoguang Wei, Lianhua Dong, Xiang Fang, Dong An, Xinhua Dai:
SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data. - Mingshuai Chen, Mingai Sun, Xi Su, Prayag Tiwari, Yijie Ding:
Fuzzy kernel evidence Random Forest for identifying pseudouridine sites. - Correction to: DeepFormer: a hybrid network based on convolutional neural network and flow-attention mechanism for identifying the function of DNA sequences.
- Adam Wright, Mark D. Wilkinson, Christopher J. Mungall, Scott Cain, Stephen Richards, Paul W. Sternberg, Ellen Provin, Jonathan L. Jacobs, Scott Geib, Daniela Raciti, Karen Yook, Lincoln Stein, David C. Molik:
FAIR Header Reference genome: a TRUSTworthy standard. - Zhanyu Xu, Haibo Liao, Liuliu Huang, Qingfeng Chen, Wei Lan, Shikang Li:
IBPGNET: lung adenocarcinoma recurrence prediction based on neural network interpretability. - Chunjiang Yu, Hui Zong, Yalan Chen, Yibin Zhou, Xingyun Liu, Yuxin Lin, Jiakun Li, Xiaonan Zheng, Hua Min, Bairong Shen:
PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data. - Xing Chen, Li Huang:
Computational model for drug research. - Zhaojia Chen, Noor ul Ain, Qian Zhao, Xingtan Zhang:
From tradition to innovation: conventional and deep learning frameworks in genome annotation. - Conghao Wang, Hiok Hian Ong, Shunsuke Chiba, Jagath C. Rajapakse:
GLDM: hit molecule generation with constrained graph latent diffusion model. - Shenjie Wang, Xiaoyan Zhu, Xuwen Wang, Yuqian Liu, Minchao Zhao, Zhili Chang, Xiaonan Wang, Yang Shao, Jiayin Wang:
TMBstable: a variant caller controls performance variation across heterogeneous sequencing samples.
Volume 25, Number 4, 2024
- Simin Xia, Dianke Li, Xinru Deng, Zhongyang Liu, Huaqing Zhu, Yuan Liu, Dong Li:
Integration of protein sequence and protein-protein interaction data by hypergraph learning to identify novel protein complexes. - Eduardo N. Castanho, Helena Aidos, Sara C. Madeira:
Biclustering data analysis: a comprehensive survey. - Qiaosi Tang, Ranjala Ratnayake, Gustavo de M. Seabra, Zhe Jiang, Ruogu Fang, Lina Cui, Yousong Ding, Tamer Kahveci, Jiang Bian, Chenglong Li, Hendrik Luesch, Yanjun Li:
Morphological profiling for drug discovery in the era of deep learning. - Ran Jia, Ying-Zan Ren, Po-Nian Li, Rui Gao, Yusen Zhang:
SCSMD: Single Cell Consistent Clustering based on Spectral Matrix Decomposition. - Yanli Wang, Jianlin Cheng:
HiCDiff: single-cell Hi-C data denoising with diffusion models. - Lequn Wang, Yaofeng Hu, Kai Xiao, Chuanchao Zhang, Qianqian Shi, Luonan Chen:
Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics. - Sudipto Baul, Khandakar Tanvir Ahmed, Qibing Jiang, Guangyu Wang, Qian Li, Jeongsik Yong, Wei Zhang:
Integrating spatial transcriptomics and bulk RNA-seq: predicting gene expression with enhanced resolution through graph attention networks. - Chengshang Lyu, Lingxi Chen, Xiaoping Liu:
Detecting tipping points of complex diseases by network information entropy. - Yue Li, Bingyan Liu, Jinyan Deng, Yi Guo, Hongbo Du:
Image-based molecular representation learning for drug development: a survey. - Bijia Chen, Chao Ren, Zhangyi Ouyang, Jingxuan Xu, Kang Xu, Yaru Li, Hejiang Guo, Xuemei Bai, Mengge Tian, Xiang Xu, Yuyang Wang, Hao Li, Xiaochen Bo, Hebing Chen:
Stratifying TAD boundaries pinpoints focal genomic regions of regulation, damage, and repair. - Hongtai Jing, Zhengtao Gao, Sheng Xu, Tao Shen, Zhangzhi Peng, Shwai He, Tao You, Shuang Ye, Wei Lin, Siqi Sun:
Accurate prediction of antibody function and structure using bio-inspired antibody language model. - Georg Hahn, Dmitry Prokopenko, Julian Hecker, Sharon Marie Lutz, Kristina Mullin, Leinal Sejour, Winston Hide, Ioannis S. Vlachos, Stacia Desantis, Rudolph E. Tanzi, Christoph Lange:
Prediction of disease-free survival for precision medicine using cooperative learning on multi-omic data. - Guoqiang Zhou, Yuke Qin, Qiansen Hong, Haoran Li, Huaming Chen, Jun Shen:
GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction. - Sarthak Jain, Sandra E. Safo:
DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification. - Qingxiong Tan, Jin Xiao, Jiayang Chen, Yixuan Wang, Zeliang Zhang, Tiancheng Zhao, Yu Li:
ifDEEPre: large protein language-based deep learning enables interpretable and fast predictions of enzyme commission numbers. - Lixin Ren, Wanbiao Ma, Yong Wang:
Predicting RNA polymerase II transcriptional elongation pausing and associated histone code. - Skylar A Gay, Gregory Ellison, Jianing Xu, Jialin Yang, Yiliang Wei, Shaoyuan Wu, Lili Yu, Christopher C. Whalen, Jonathan Arnold, Liang Liu:
Phylogenetic inference of inter-population transmission rates for infectious diseases. - Qiang Yang, Long Xu, Weihe Dong, Xiaokun Li, Kuanquan Wang, Suyu Dong, Xianyu Zhang, Tiansong Yang, Feng Jiang, Bin Zhang, Gongning Luo, Xin Gao, Guohua Wang:
HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses. - Ru Zhang, Yanmei Lin, Yijia Wu, Lei Deng, Hao Zhang, Mingzhi Liao, Yuzhong Peng:
MvMRL: a multi-view molecular representation learning method for molecular property prediction. - Feng Jiang, Yuzhi Guo, Hehuan Ma, Saiyang Na, Wenliang Zhong, Yi Han, Tao Wang, Junzhou Huang:
GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity. - Joshua Beals, Haiyan Hu, Xiaoman Li:
A survey of experimental and computational identification of small proteins. - Xiangru Tang, Howard Dai, Elizabeth Knight, Fang Wu, Yunyang Li, Tianxiao Li, Mark Gerstein:
A survey of generative AI for de novo drug design: new frontiers in molecule and protein generation. - Lei Li, Jiayi Sun, Yanbin Fu, Siriruk Changrob, Joshua J. C. McGrath, Patrick C. Wilson:
A hybrid demultiplexing strategy that improves performance and robustness of cell hashing. - Zhihao Si, Hanshuang Li, Wenjing Shang, Yanan Zhao, Lingjiao Kong, Chunshen Long, Yongchun Zuo, Zhenxing Feng:
SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network. - Ao Shen, Mingzhi Yuan, Yingfan Ma, Jie Du, Manning Wang:
Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction. - Haiping Zhang, Hongjie Fan, Jixia Wang, Tao Hou, Konda Mani Saravanan, Wei Xia, Hei Wun Kan, Junxin Li, John Z. H. Zhang, Xinmiao Liang, Yang Chen:
Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery. - Jun Teng, Tingting Zhai, Xinyi Zhang, Changheng Zhao, Wenwen Wang, Hui Tang, Dan Wang, Yingli Shang, Chao Ning, Qin Zhang:
Improving multi-population genomic prediction accuracy using multi-trait GBLUP models which incorporate global or local genetic correlation information. - Steven Allers, Kyle A. O'Connell, Thad B. Carlson, David Belardo, Benjamin L. King:
Reusable tutorials for using cloud-based computing environments for the analysis of bacterial gene expression data from bulk RNA sequencing. - Md. Mamunur Rashid, Kumar Selvarajoo:
Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data. - Correction to: Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model.
- Jing-Tian Wang, Xiao-Yu Chang, Qiong Zhao, Yuan-Ming Zhang:
FastBiCmrMLM: a fast and powerful compressed variance component mixed logistic model for big genomic case-control genome-wide association study. - Alen Suljic, Tomaz Mark Zorec, Samo Zakotnik, Doroteja Vlaj, Rok Kogoj, Natasa Knap, Miroslav Petrovec, Mario Poljak, Tatjana Avsic-Zupanc, Misa Korva:
Efficient SARS-CoV-2 variant detection and monitoring with Spike Screen next-generation sequencing. - Zhengfa Xue, Aifen Zhou, Xiaoyan Zhu, Linxuan Li, Huanhuan Zhu, Xin Jin, Jiayin Wang:
NIPT-PG: empowering non-invasive prenatal testing to learn from population genomics through an incremental pan-genomic approach. - Jiashuo Wu, Xilong Zhao, Yalan He, Bingyue Pan, Jiyin Lai, Miao Ji, Siyuan Li, Junling Huang, Junwei Han:
IDMIR: identification of dysregulated miRNAs associated with disease based on a miRNA-miRNA interaction network constructed through gene expression data. - Siwen Wu, Jun-tao Guo:
Improved prediction of DNA and RNA binding proteins with deep learning models. - Christophe Boetto, Arthur Frouin, Léo Henches, Antoine Auvergne, Yuka Suzuki, Etienne Patin, Marius Bredon, Alec Chiu, Milieu Interieur Consortium, Sriram Sankararaman, Noah Zaitlen, Sean P. Kennedy, Lluis Quintana-Murci, Darragh Duffy, Harry Sokol, Hugues Aschard:
MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data. - Junyao Jiang, Jinlian Li, Sunan Huang, Fan Jiang, Yanran Liang, Xueli Xu, Jie Wang:
CACIMAR: cross-species analysis of cell identities, markers, regulations, and interactions using single-cell RNA sequencing data. - Shengbo Wu, Haonan Zhou, Danlei Chen, Yutong Lu, Yanni Li, Jianjun Qiao:
Multi-omic analysis tools for microbial metabolites prediction. - Jacob J. E. Koopman, Katherine A Buhler, May Y. Choi:
From machine learning to clinical practice: phenotypic clusters of anti-MDA5 antibody-positive dermatomyositis. - Jilong Bian, Hao Lu, Guanghui Dong, Guohua Wang:
Hierarchical multimodal self-attention-based graph neural network for DTI prediction. - Pi-Jing Wei, Ziqiang Guo, Zhen Gao, Zheng Ding, Rui-Fen Cao, Yansen Su, Chun-Hou Zheng:
Inference of gene regulatory networks based on directed graph convolutional networks. - Qirui Guo, Musu Yuan, Lei Zhang, Minghua Deng:
scPLAN: a hierarchical computational framework for single transcriptomics data annotation, integration and cell-type label refinement. - Liang Cheng:
Attention mechanism models for precision medicine. - Euiseong Ko, Youngsoon Kim, Farhad Shokoohi, Tesfaye B. Mersha, Mingon Kang:
SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis. - Weibing Wang, Yusen Ye, Lin Gao:
Statistical modeling and significance estimation of multi-way chromatin contacts with HyperloopFinder. - Baohui Lin, Xiaoling Luo, Yumeng Liu, Xiaopeng Jin:
A comprehensive review and comparison of existing computational methods for protein function prediction. - Emily McLeish, Nataliya Slater, Frank L. Mastaglia, Merrilee Needham, Jerome D. Coudert:
Balancing Clinical Applicability and Scientific Depth in ML Models for MDA5-DM Prognosis. - Yujin Kim, Minwoo Jeong, In Gyeong Koh, Chanhee Kim, Hyeji Lee, Jae Hyun Kim, Ronald Yurko, Il Bin Kim, Jeongbin Park, Donna M. Werling, Stephan J. Sanders, Joon-Yong An:
CWAS-Plus: estimating category-wide association of rare noncoding variation from whole-genome sequencing data with cell-type-specific functional data. - Mengxin Zheng, Guicong Sun, Xueping Li, Yongxian Fan:
EGPDI: identifying protein-DNA binding sites based on multi-view graph embedding fusion. - Jaeyoon Kim, Junhee Seok:
ctGAN: combined transformation of gene expression and survival data with generative adversarial network. - Zihan Dong, Wei Jiang, Hongyu Li, Andrew T. Dewan, Hongyu Zhao:
LDER-GE estimates phenotypic variance component of gene-environment interactions in human complex traits accurately with GE interaction summary statistics and full LD information. - Xinjia Ruan, Yu Cheng, Yuqing Ye, Yuhang Wang, Xinyi Chen, Yuqing Yang, Tiantian Liu, Fangrong Yan:
PIPET: predicting relevant subpopulations in single-cell data using phenotypic information from bulk data. - Heng Hu, Runtian Gao, Wentao Gao, Bo Gao, Zhongjun Jiang, Murong Zhou, Guohua Wang, Tao Jiang:
SVDF: enhancing structural variation detect from long-read sequencing via automatic filtering strategies. - Ryan P. Seaman, Ross Campbell, Valena Doe, Zelaikha B. Yosufzai, Joel H. Graber:
A cloud-based training module for efficient de novo transcriptome assembly using Nextflow and Google cloud. - Sara Joubbi, Alessio Micheli, Paolo Milazzo, Giuseppe Maccari, Giorgio Ciano, Dario Cardamone, Duccio Medini:
Antibody design using deep learning: from sequence and structure design to affinity maturation. - Regina Nóra Fiam, Csabai István, Norbert Solymosi:
Comparing full variation profile analysis with the conventional consensus method in SARS-CoV-2 phylogeny. - Ali H. Rafati, Sâmia Joca, Regina T. Vontell, Gregers Wegener, Maryam Ardalan:
Approaches to embryonic neurodevelopment: from neural cell to neural tube formation through mathematical models. - Han Wang, Chang Li, Xinyu Yu, Jingyang Gao:
Deletion variants calling in third-generation sequencing data based on a dual-attention mechanism. - Paolo Battuello, Giorgio Corti, Alice Bartolini, Annalisa Lorenzato, Alberto Sogari, Mariangela Russo, Federica Di Nicolantonio, Alberto Bardelli, Giovanni Crisafulli:
Mutational signatures of colorectal cancers according to distinct computational workflows. - Hwiyoung Lee, Tianzhou Ma, Hongjie Ke, Zhenyao Ye, Shuo Chen:
dCCA: detecting differential covariation patterns between two types of high-throughput omics data. - Maria Alexandra Rujano, Jan-Willem Boiten, Christian Ohmann, Steve Canham, Sergio Contrino, Romain David, Jonathan Ewbank, Claudia Filippone, Claire Connellan, Ilse Custers, Rick van Nuland, Michaela Theresia Mayrhofer, Petr Holub, Eva García Álvarez, Emmanuel Bacry, Nigel Hughes, Mallory Ann Freeberg, Birgit Schaffhauser, Harald Wagener, Alex Sánchez-Pla, Guido Bertolini, Maria Panagiotopoulou:
Sharing sensitive data in life sciences: an overview of centralized and federated approaches. - Sandeep Singh, Xinrui Shi, Samuel Haddox, Justin Elfman, Syed Basil Ahmad, Sarah Lynch, Tommy Manley, Claire Piczak, Christopher Phung, Yunan Sun, Aadi Sharma, Hui Li:
RTCpredictor: identification of read-through chimeric RNAs from RNA sequencing data. - Asif M. Khan, Esra Büsra Isik, Tin Wee Tan:
A global initiative on addressing bioinformatics' grand challenges. - Zeqian Li, Shilong Wang, Hai Cui, Xiaoxia Liu, Yijia Zhang:
Spatiotemporal constrained RNA-protein heterogeneous network for protein complex identification. - Meihui Tian, Weifang Sun, Yinhui Mao, Yanan Zhang, Huan Liu, Yong Tang:
Mechanistic study of acupuncture on the pterygopalatine ganglion to improve allergic rhinitis: analysis of multi-target effects based on bioinformatics/network topology strategie. - Ishtiaque Ahammad, Anika Bushra Lamisa, Arittra Bhattacharjee, Tabassum Binte Jamal, Md Shamsul Arefin, Zeshan Mahmud Chowdhury, Mohammad Uzzal Hossain, Keshob Chandra Das, Chaman Ara Keya, Md. Salimullah:
AITeQ: a machine learning framework for Alzheimer's prediction using a distinctive five-gene signature. - Catalina Gonzalez Gomez, Manuel Rosa-Calatrava, Julien Fouret:
Optimizing in silico drug discovery: simulation of connected differential expression signatures and applications to benchmarking. - Yuan Gao, Bin Ma, Qianshuai Xu, Yuna Peng, Huimin Gong, Aohan Guan, Kexin Hua, Paul R. Langford, Hui Jin, Rui Luo:
Spatial proximity and gene function: a new dimension in prokaryotic gene association network analysis with 3D-GeneNet. - Juok Cho, Bukyung Baik, Hai C. T. Nguyen, Daeui Park, Dougu Nam:
Characterizing efficient feature selection for single-cell expression analysis. - Michal Zawisza-Álvarez, Jesús Peñuela-Melero, Esteban Vegas, Ferran Reverter, Jordi Garcia-Fernàndez, Carlos Herrera-Úbeda:
Exploring functional conservation in silico: a new machine learning approach to RNA-editing. - En-Yu Lai, Yen-Tsung Huang:
Identifying pleiotropic genes via the composite test amidst the complexity of polygenic traits. - Sagar Gupta, Veerbhan Kesarwani, Umesh Bhati, Jyoti, Ravi Shankar:
PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants. - Lei Zhang, Shu Liang, Lin Wan:
A multi-view graph contrastive learning framework for deciphering spatially resolved transcriptomics data. - Rajan Gyawali, Ashwin Dhakal, Liguo Wang, Jianlin Cheng:
CryoSegNet: accurate cryo-EM protein particle picking by integrating the foundational AI image segmentation model and attention-gated U-Net. - Mostafa Herajy, Fei Liu, Monika Heiner:
Design patterns for the construction of computational biological models. - Quyuan Tao, Yiheng Xu, Youzhe He, Ting Luo, Xiaoming Li, Lei Han:
Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data. - Alexei Novoloaca, Camilo Broc, Laurent Beloeil, Wen-Han Yu, Jérémie Becker:
Comparative analysis of integrative classification methods for multi-omics data. - Junbo Duan, Xinrui Zhao, Xiaoming Wu:
LoRA-TV: read depth profile-based clustering of tumor cells in single-cell sequencing. - Nanjun Chen, Jixiang Yu, Zhe Liu, Fuzhou Wang, Xiangtao Li, Ka-Chun Wong:
TP-LMMSG: a peptide prediction graph neural network incorporating flexible amino acid property representation. - Guoqing Zhang, Hui Wang, Zhiguo Zhang, Lu Zhang, Guibing Guo, Jian Yang, Fajie Yuan, Feng Ju:
Highly accurate classification and discovery of microbial protein-coding gene functions using FunGeneTyper: an extensible deep learning framework. - Hegang Chen, Yuyin Lu, Zhiming Dai, Yuedong Yang, Qing Li, Yanghui Rao:
Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological hierarchy knowledge. - Tao Wang, Han Shu, Jialu Hu, Yongtian Wang, Jin Chen, Jiajie Peng, Xuequn Shang:
Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster. - Yahui Lei, Xiao-Tai Huang, Xingli Guo, Kei Hang Katie Chan, Lin Gao:
DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations. - Aleksandr Ianevski, Aleksandr Kushnir, Kristen Nader, Mitro Miihkinen, Henri Xhaard, Tero Aittokallio, ZiaurRehman Tanoli:
RepurposeDrugs: an interactive web-portal and predictive platform for repurposing mono- and combination therapies. - Correction to: Prediction of protein-ligand binding affinity via deep learning models.
- Yue Xi, Kun Zheng, Fulan Deng, Yujun Liu, Hourong Sun, Yingxia Zheng, Henry H. Y. Tong, Yuan Ji, Yingchun Zhang, Wantao Chen, Yiming Zhang, Xin Zou, Jie Hao:
Themis: advancing precision oncology through comprehensive molecular subtyping and optimization. - Bowen Li, Zhen Wang, Ziqi Liu, Yanxin Tao, Chulin Sha, Min He, Xiaolin Li:
DrugMetric: quantitative drug-likeness scoring based on chemical space distance. - Miguel Hernandez-Gamarra, Alba Salgado-Roo, Eduardo Dominguez, Elena María Goiricelaya Seco, Sara Veiga-Rúa, Lucía F. Pedrera-Garbayo, Ángel Carracedo, Catarina Allegue:
CARTAR: a comprehensive web tool for identifying potential targets in chimeric antigen receptor therapies using TCGA and GTEx data. - Dezhen Zhang, Shuhua Gao, Zhi-Ping Liu, Rui Gao:
LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data. - Leandro A Bugnon, Leandro E. Di Persia, Matias Gerard, Jonathan Raad, Santiago Prochetto, Emilio Fenoy, Uciel Chorostecki, Federico Ariel, Georgina Stegmayer, Diego H. Milone:
sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure. - Guangyu Li, Jiayu Wu, Xiaoyue Wang:
Predicting functional UTR variants by integrating region-specific features. - Lea Eckhart, Kerstin Lenhof, Lisa-Marie Rolli, Hans-Peter Lenhof:
A comprehensive benchmarking of machine learning algorithms and dimensionality reduction methods for drug sensitivity prediction. - Chuiqin Fan, Fuyi Chen, Yuanguo Chen, Liangping Huang, Manna Wang, Yulin Liu, Yu Wang, Huijie Guo, Nanpeng Zheng, Yanbing Liu, Hongwu Wang, Lian Ma:
irGSEA: the integration of single-cell rank-based gene set enrichment analysis. - Ruofan Jin, Qing Ye, Jike Wang, Zheng Cao, Dejun Jiang, Tianyue Wang, Yu Kang, Wanting Xu, Chang-Yu Hsieh, Tingjun Hou:
AttABseq: an attention-based deep learning prediction method for antigen-antibody binding affinity changes based on protein sequences. - Renmao Tian, Jizhong Zhou, Behzad Imanian:
PlasmidHunter: accurate and fast prediction of plasmid sequences using gene content profile and machine learning. - Gregory Tong, Nasun Hah, Thomas F. Martinez:
Comparison of software packages for detecting unannotated translated small open reading frames by Ribo-seq. - Raghvendra Mall, Ankita Singh, Chirag N. Patel, Gregory Guirimand, Filippo Castiglione:
VISH-Pred: an ensemble of fine-tuned ESM models for protein toxicity prediction. - Wenkai Xiang, Feisheng Zhong, Lin Ni, Mingyue Zheng, Xutong Li, Qian Shi, Dingyan Wang:
Gram matrix: an efficient representation of molecular conformation and learning objective for molecular pretraining. - Cheng Zhu, Chengyun Zhang, Tianfeng Shang, Chenhao Zhang, Silong Zhai, Lujing Cao, Zhenyu Xu, Zhihao Su, Ying Song, An Su, Chengxi Li, Hongliang Duan:
GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach. - Montserrat Goles, Anamaria Sanchez-Daza, Gabriel Cabas-Mora, Lindybeth Sarmiento-Varón, Julieta Sepúlveda-Yañez, Hoda Anvari-Kazemabad, Mehdi D. Davari, Roberto Uribe Paredes, Alvaro Olivera-Nappa, Marcelo A. Navarrete, David Medina-Ortiz:
Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides. - Runzhou Yu, Ziyi Huang, Theo Y. C. Lam, Yanni Sun:
Utilizing profile hidden Markov model databases for discovering viruses from metagenomic data: a comprehensive review. - Onder Tutsoy, Hilmi Erdem Sumbul:
A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data.
Volume 25, Number 5, 2024
- Aishwarya Budhkar, Ziyang Tang, Xiang Liu, Xuhong Zhang, Jing Su, Qianqian Song:
xSiGra: explainable model for single-cell spatial data elucidation. - Linlin Hou, Hongxin Xiang, Xiangxiang Zeng, Dongsheng Cao, Li Zeng, Bosheng Song:
Attribute-guided prototype network for few-shot molecular property prediction. - Qitao Jia, Yuanling Xia, Fanglin Dong, Weihua Li:
MetaFluAD: meta-learning for predicting antigenic distances among influenza viruses. - Wentao Cui, Qingqing Long, Meng Xiao, Xuezhi Wang, Guihai Feng, Xin Li, Pengfei Wang, Yuanchun Zhou:
Refining computational inference of gene regulatory networks: integrating knockout data within a multi-task framework. - Zeyu Lu, Xue Xiao, Qiang Zheng, Xinlei Wang, Lin Xu:
Assessing next-generation sequencing-based computational methods for predicting transcriptional regulators with query gene sets. - Xinze Liu, Jingxuan Shi, Yuanyuan Jiao, Jiaqi An, Jingwei Tian, Yue Yang, Li Zhuo:
Integrated multi-omics with machine learning to uncover the intricacies of kidney disease. - Lei Chen, Jiahui Gu, Bo Zhou:
PMiSLocMF: predicting miRNA subcellular localizations by incorporating multi-source features of miRNAs. - Yida Wu, Da Zhou, Jie Hu:
Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data. - Kerstin Lenhof, Lea Eckhart, Lisa-Marie Rolli, Hans-Peter Lenhof:
Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer. - Fenglei Li, Qiaoyu Hu, Yongqi Zhou, Hao Yang, Fang Bai:
DiffPROTACs is a deep learning-based generator for proteolysis targeting chimeras. - Yang Xu, Yuxiang Zhang, Yanru Cui, Kai Zhou, Guangning Yu, Wenyan Yang, Xin Wang, Furong Li, Xiusheng Guan, Xuecai Zhang, Zefeng Yang, Shizhong Xu, Chenwu Xu:
GA-GBLUP: leveraging the genetic algorithm to improve the predictability of genomic selection. - Weihong Huang, Feng Yang, Qiang Zhang, Juan Liu:
A dual-scale fused hypergraph convolution-based hyperedge prediction model for predicting missing reactions in genome-scale metabolic networks. - Katarzyna Nalecz-Charkiewicz, Kamil Charkiewicz, Robert M. Nowak:
Quantum computing in bioinformatics: a systematic review mapping. - Marwan Abdellah, Alessandro Foni, Juan Jose Garcia-Cantero, Nadir Román Guerrero, Elvis Boci, Adrien Fleury, Jay S. Coggan, Daniel X. Keller, Judit Planas, Jean-Denis Courcol, Georges Khazen:
Synthesis of geometrically realistic and watertight neuronal ultrastructure manifolds for in silico modeling. - Harrison Anthony, Cathal Seoighe:
Performance assessment of computational tools to detect microsatellite instability. - Yiran Huang, Yufu Lin, Wei Lan, Cuiyu Huang, Cheng Zhong:
GloEC: a hierarchical-aware global model for predicting enzyme function. - Xinglong Wang, Kangjie Xu, Xuan Zeng, Kai Linghu, Beichen Zhao, Shangyang Yu, Kun Wang, Shuyao Yu, Xinyi Zhao, Weizhu Zeng, Kai Wang, Jingwen Zhou:
Machine learning-assisted substrate binding pocket engineering based on structural information. - Weining Lu, Yin Tang, Yu Liu, Shiyi Lin, Qifan Shuai, Bin Liang, Rongqing Zhang, Yu Cheng, Dong Fang:
CatLearning: highly accurate gene expression prediction from histone mark. - Benjamin Tam, Philip Naderev P. Lagniton, Mariano Da Luz, Bojin Zhao, Siddharth Sinha, Chon Lok Lei, San Ming Wang:
Comprehensive classification of TP53 somatic missense variants based on their impact on p53 structural stability. - Zhaohan Meng, Siwei Liu, Shangsong Liang, Bhautesh Jani, Zaiqiao Meng:
Heterogeneous biomedical entity representation learning for gene-disease association prediction. - Shunjie Zhang, Pan Li, Shenghan Wang, Jijun Zhu, Zhongting Huang, Fuqiang Cai, Sebastian Freidel, Fei Ling, Emanuel Schwarz, Junfang Chen:
BioM2: biologically informed multi-stage machine learning for phenotype prediction using omics data. - Noura Aherrahrou, Hamid Tairi, Zouhair Aherrahrou:
Genomic privacy preservation in genome-wide association studies: taxonomy, limitations, challenges, and vision. - Xinhao Yi, Siwei Liu, Yu Wu, Douglas McCloskey, Zaiqiao Meng:
BPP: a platform for automatic biochemical pathway prediction. - James S. L. Browning Jr., Daniel R. Tauritz, John Beckmann:
Evolutionary algorithms simulating molecular evolution: a new field proposal. - Yan Miao, Zhenyuan Sun, Chen Lin, Haoran Gu, Chenjing Ma, Yingjian Liang, Guohua Wang:
DeePhafier: a phage lifestyle classifier using a multilayer self-attention neural network combining protein information. - Vijini Mallawaarachchi, Anuradha Wickramarachchi, Hansheng Xue, Bhavya Nalagampalli Papudeshi, Susanna R. Grigson, George Bouras, Rosa E. Prahl, Anubhav Kaphle, Andrey Verich, Berenice Talamantes-Becerra, Elizabeth A. Dinsdale, Robert A. Edwards:
Solving genomic puzzles: computational methods for metagenomic binning. - Correction to: Accurate prediction of antibody function and structure using bio-inspired antibody language model.
- Yuxing Wang, Junhan Zhao, Hongye Xu, Cheng Han, Zhiqiang Tao, Dawei Zhou, Tong Geng, Dongfang Liu, Zhicheng Ji:
A systematic evaluation of computational methods for cell segmentation. - Ruohan Ren, Hongyu Yu, Jiahao Teng, Sihui Mao, Zixuan Bian, Yangtianze Tao, Stephen S.-T. Yau:
CAPE: a deep learning framework with Chaos-Attention net for Promoter Evolution. - Zhenze Liu, Yingjian Liang, Guohua Wang, Tianjiao Zhang:
scLEGA: an attention-based deep clustering method with a tendency for low expression of genes on single-cell RNA-seq data. - Liang Liu, Peiqing Sun, Wei Zhang:
A pan-cancer interrogation of intronic polyadenylation and its association with cancer characteristics. - Keyun Zhu, Mengting Huang, Yimeng Wang, Yaxin Gu, Weihua Li, Guixia Liu, Yun Tang:
MetaPredictor: in silico prediction of drug metabolites based on deep language models with prompt engineering. - Chang Dou, Yijie Yang, Fei Zhu, Bingzhi Li, Yuping Duan:
Explorer: efficient DNA coding by De Bruijn graph toward arbitrary local and global biochemical constraints. - Yuefan Lin, Zixiang Pan, Yuansong Zeng, Yuedong Yang, Zhiming Dai:
Detecting novel cell type in single-cell chromatin accessibility data via open-set domain adaptation. - Suronjit Kumar Roy, Mohammad Shahangir Biswas, Md Foyzur Raman, Rubait Hasan, Zahidur Rahmann, Md Moyen Uddin Pk:
A computational approach to developing a multi-epitope vaccine for combating Pseudomonas aeruginosa-induced pneumonia and sepsis. - Xiaobao Ding, Lin Zhang, Ming Fan, Lihua Li:
TME-NET: an interpretable deep neural network for predicting pan-cancer immune checkpoint inhibitor responses. - Yinyin Wang, Yihang Sui, Jiaqi Yao, Hong Jiang, Qimeng Tian, Yun Tang, Yongyu Ou, Jing Tang, Ninghua Tan:
Herb-CMap: a multimodal fusion framework for deciphering the mechanisms of action in traditional Chinese medicine using Suhuang antitussive capsule as a case study. - Margarita Pertseva, Oceane Follonier, Daniele Scarcella, Sai T. Reddy:
TCR clustering by contrastive learning on antigen specificity. - Jingjing Wang, Zhijiang Yang, Chang Chen, Ge Yao, Xiukun Wan, Shaoheng Bao, Junjie Ding, Liangliang Wang, Hui Jiang:
MPEK: a multitask deep learning framework based on pretrained language models for enzymatic reaction kinetic parameters prediction. - Young Su Ko, Jonathan Parkinson, Cong Liu, Wei Wang:
TUnA: an uncertainty-aware transformer model for sequence-based protein-protein interaction prediction. - Yang Li, Anjun Ma, Yizhong Wang, Qi Guo, Cankun Wang, Hongjun Fu, Bingqiang Liu, Qin Ma:
Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data. - Feng Li, Jingwen Wang, Mengyue Li, Xiaomeng Zhang, Yongjuan Tang, Xinyu Song, Yifang Zhang, Liying Pei, Jiaqi Liu, Chunlong Zhang, Xia Li, Yanjun Xu, Yunpeng Zhang:
Identifying cell type-specific transcription factor-mediated activity immune modules reveal implications for immunotherapy and molecular classification of pan-cancer. - Qiqing Fu, Chenyu Dong, Yunhe Liu, Xiaoqiong Xia, Gang Liu, Fan Zhong, Lei Liu:
A comparison of scRNA-seq annotation methods based on experimentally labeled immune cell subtype dataset. - Correction to: Revolutionizing GPCR-ligand predictions: DeepGPCR with experimental validation for high-precision drug discovery.
Volume 25, Number 6, 2024
- Yabin Kuang, Minzhu Xie:
DrugDoctor: enhancing drug recommendation in cold-start scenario via visit-level representation learning and training. - Aliaa E. Ali, Li-Li Li, Michael J. Courtney, Olli T. Pentikäinen, Pekka A. Postila:
Atomistic simulations reveal impacts of missense mutations on the structure and function of SynGAP1. - Musaddiq K. Lodi, Anna Chernikov, Preetam Ghosh:
COFFEE: consensus single cell-type specific inference for gene regulatory networks. - Jessica Butts, Leif Verace, Christine Wendt, Russell P. Bowler, Craig P. Hersh, Qi Long, Lynn E. Eberly, Sandra E. Safo:
HIP: a method for high-dimensional multi-view data integration and prediction accounting for subgroup heterogeneity. - Michael Y. Fatemi, Yunrui Lu, Alos B. Diallo, Gokul Srinivasan, Zarif L. Azher, Brock C. Christensen, Lucas A. Salas, Gregory J. Tsongalis, Scott Palisoul, Laurent Perreard, Frederick W. Kolling IV, Louis J. Vaickus, Joshua J. Levy:
An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology. - Wen Wen, Jiaxin Zhong, Zhaoxi Zhang, Lijuan Jia, Tinyi Chu, Nating Wang, Charles G. Danko, Zhong Wang:
dHICA: a deep transformer-based model enables accurate histone imputation from chromatin accessibility. - Yike Shen, Arce Domingo-Relloso, Allison Kupsco, Marianthi-Anna Kioumourtzoglou, Maria Tellez-Plaza, Jason G. Umans, Amanda M. Fretts, Ying Zhang, Peter F. Schnatz, Ramon Casanova, Lisa Warsinger Martin, Steve Horvath, JoAnn E. Manson, Shelley A Cole, Haotian Wu, Eric A Whitsel, Andrea A. Baccarelli, Ana Navas-Acien, Feng Gao:
AESurv: autoencoder survival analysis for accurate early prediction of coronary heart disease. - Yanghong Guo, Bencong Zhu, Chen Tang, Ruichen Rong, Ying Ma, Guanghua Xiao, Lin Xu, Qiwei Li:
BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data. - Leqi Tian, Jiashun Xiao, Tianwei Yu:
A robust statistical approach for finding informative spatially associated pathways. - Yao Yao, Youhua Frank Chen, Qingpeng Zhang:
Optimized patient-specific immune checkpoint inhibitor therapies for cancer treatment based on tumor immune microenvironment modeling. - Kai Shi, Qiaohui Liu, Qingrong Ji, Qisheng He, Xing-Ming Zhao:
MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework. - Giulia Russo, Elena Crispino, Silvia Casati, Emanuela Corsini, Andrew Worth, Francesco Pappalardo:
Pioneering bioinformatics with agent-based modelling: an innovative protocol to accurately forecast skin or respiratory allergic reactions to chemical sensitizers. - Zhengzheng Lou, Xiaojiao Wei, Yuanhao Hu, Shizhe Hu, Yucong Wu, Zhen Tian:
Clustering scRNA-seq data with the cross-view collaborative information fusion strategy. - Yourui Han, Bolin Chen, Jun Bian, Ruiming Kang, Xuequn Shang:
Cancerous time estimation for interpreting the evolution of lung adenocarcinoma. - Zuqi Li, Sonja Katz, Edoardo Saccenti, David W. Fardo, Peter Claes, Vítor A. P. Martins dos Santos, Kristel Van Steen, Gennady V. Roshchupkin:
Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping. - Shisheng Wang, Wenjuan Zeng, Yin Yang, Jingqiu Cheng, Dan Liu, Hao Yang:
DEWNA: dynamic entropy weight network analysis and its application to the DNA-binding proteome in A549 cells with cisplatin-induced damage. - Sheng Liu, Hye Seung Nam, Ziyu Zeng, Xuehong Deng, Elnaz Pashaei, Yong Zang, Lei Yang, Chenglong Li, Jiaoti Huang, Michael K. Wendt, Xin Lu, Rong Huang, Jun Wan:
CDHu40: a novel marker gene set of neuroendocrine prostate cancer. - Guo Wei, Nannan Wu, Kunyang Zhao, Sihai Yang, Long Wang, Yan Liu:
DeepCheck: multitask learning aids in assessing microbial genome quality. - Marco Ruscone, Andrea Checcoli, Randy W. Heiland, Emmanuel Barillot, Paul Macklin, Laurence Calzone, Vincent Noël:
Building multiscale models with PhysiBoSS, an agent-based modeling tool. - Zhen Wang, Ziqi Liu, Wei Zhang, Yanjun Li, Yizhen Feng, Shaokang Lv, Han Diao, Zhaofeng Luo, Pengju Yan, Min He, Xiaolin Li:
AptaDiff: de novo design and optimization of aptamers based on diffusion models. - Anchen Sun, Elizabeth J. Franzmann, Zhibin Chen, Xiaodong Cai:
Deep contrastive learning for predicting cancer prognosis using gene expression values. - Boyi Yu, Genta Nagae, Yutaka Midorikawa, Kenji Tatsuno, Bhaskar Dasgupta, Hiroyuki Aburatani, Hiroki Ueda:
m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data. - Elena Domínguez Romero, Stanislav Mazurenko, Martin Scheringer, Vítor A. P. Martins dos Santos, Chris T. A. Evelo, Mihail Anton, John M. Hancock, Anze Zupanic, María Suárez-Diez:
Making PBPK models more reproducible in practice. - Thomas Konstantinovsky, Ayelet Peres, Pazit Polak, Gur Yaari:
An unbiased comparison of immunoglobulin sequence aligners. - Felipe Campelo, Ana Laura Grossi de Oliveira, João Reis-Cunha, Vanessa Gomes Fraga, Pedro Henrique Bastos, Jodie Ashford, Anikó Ekárt, Talita Emile Ribeiro Adelino, Marcos Vinicius Ferreira Silva, Felipe Campos de Melo Iani, Augusto César Parreiras de Jesus, Daniella Castanheira Bartholomeu, Giliane de Souza Trindade, Ricardo T. Fujiwara, Lilian Lacerda Bueno, Francisco Pereira Lobo:
Phylogeny-aware linear B-cell epitope predictor detects targets associated with immune response to orthopoxviruses. - Xin-Fei Wang, Lan Huang, Yan Wang, Ren-Chu Guan, Zhu-Hong You, Nan Sheng, Xuping Xie, Qixing Yang:
A multichannel graph neural network based on multisimilarity modality hypergraph contrastive learning for predicting unknown types of cancer biomarkers. - Lan Huang, Xin-Fei Wang, Yan Wang, Ren-Chu Guan, Nan Sheng, Xuping Xie, Lei Wang, Ziqi Zhao:
A multi-task prediction method based on neighborhood structure embedding and signed graph representation learning to infer the relationship between circRNA, miRNA, and cancer. - Sisi Shao, Pedro Henrique Ribeiro, Christina M. Ramirez, Jason H. Moore:
A review of feature selection strategies utilizing graph data structures and Knowledge Graphs. - Limuxuan He, Quan Zou, Qi Dai, Shuang Cheng, Yansu Wang:
Adversarial regularized autoencoder graph neural network for microbe-disease associations prediction. - Bo Li, Yong Zhang, Qing Wang, Chengyang Zhang, Mengran Li, Guangyu Wang, Qianqian Song:
Gene expression prediction from histology images via hypergraph neural networks. - Rebecca Ting Jiin Loo, Mohamed Soudy, Francesco Nasta, Mirco Macchi, Enrico Glaab:
Bioinformatics approaches for studying molecular sex differences in complex diseases. - Yumiao Gao, Siran Zhu, Huichun Li, Xueting Hao, Wen Chen, Deng Pan, Zhikang Qian:
AntigenBoost: enhanced mRNA-based antigen expression through rational amino acid substitution. - Wenyu Zhang, Yijie Ding, Leyi Wei, Xiaoyi Guo, Fengming Ni:
Therapeutic peptides identification via kernel risk sensitive loss-based k-nearest neighbor model and multi-Laplacian regularization. - Haochen Ning, Ian Boyes, Ibrahim Numanagic, Michael Rott, Li Xing, Xuekui Zhang:
Diagnostics of viral infections using high-throughput genome sequencing data. - Simone Rancati, Giovanna Nicora, Mattia Prosperi, Riccardo Bellazzi, Marco Salemi, Simone Marini:
Forecasting dominance of SARS-CoV-2 lineages by anomaly detection using deep AutoEncoders. - Lonneke Scheffer, Eric Emanuel Reber, Brij Bhushan Mehta, Milena Pavlovic, Maria Chernigovskaya, Eve Richardson, Rahmad Akbar, Fridtjof Lund-Johansen, Victor Greiff, Ingrid Hobæk Haff, Geir Kjetil Sandve:
Predictability of antigen binding based on short motifs in the antibody CDRH3. - Laiyi Fu, Zhiyuan Yao, Yangyi Zhou, Qinke Peng, Hongqiang Lyu:
ACLNDA: an asymmetric graph contrastive learning framework for predicting noncoding RNA-disease associations in heterogeneous graphs. - Gulshan Kumar Sharma, Rakesh Sharma, Kavita Joshi, Sameer Qureshi, Shubhita Mathur, Sharad Sinha, Samit Chatterjee, Vandana Nunia:
Advancing microbial diagnostics: a universal phylogeny guided computational algorithm to find unique sequences for precise microorganism detection. - Fang Yang, Chaoqun Li, Wanting Yang, Yumei He, Liping Wu, Kui Jiang, Chao Sun:
Development and validation of an explainable machine learning model for predicting multidimensional frailty in hospitalized patients with cirrhosis. - Lucas Kook, Anton Rask Lundborg:
Algorithm-agnostic significance testing in supervised learning with multimodal data. - Linjie Wang, Wei Li, Fanghui Zhou, Kun Yu, Chaolu Feng, Dazhe Zhao:
nsDCC: dual-level contrastive clustering with nonuniform sampling for scRNA-seq data analysis. - Pablo Perdomo-Quinteiro, Alberto Belmonte-Hernández:
Knowledge Graphs for drug repurposing: a review of databases and methods. - Tianxiang Liu, Cangzhi Jia, Yue Bi, Xudong Guo, Quan Zou, Fuyi Li:
scDFN: enhancing single-cell RNA-seq clustering with deep fusion networks. - Anjana Kushwaha, Patrice Duroux, Véronique Giudicelli, Konstantin Todorov, Sofia Kossida:
IMGT/RobustpMHC: robust training for class-I MHC peptide binding prediction. - Peng Ran, Yunzhi Wang, Kai Li, Shiman He, Subei Tan, Jiacheng Lv, Jiajun Zhu, Shaoshuai Tang, Jinwen Feng, Zhaoyu Qin, Yan Li, Lin Huang, Yanan Yin, Lingli Zhu, Wenjun Yang, Chen Ding:
STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data. - Wei Liu, Bo Wang, Yuting Bai, Xiao Liang, Li Xue, Jiawei Luo:
SpaGIC: graph-informed clustering in spatial transcriptomics via self-supervised contrastive learning. - André Borges Farias, Gustavo Sganzerla Martinez, Edgardo Galán-Vásquez, Marisa Fabiana Nicolás, Ernesto Pérez-Rueda:
Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability. - Zhiwei Rong, Jiali Song, Yipe Yu, Lan Mi, Mantang Qiu, Yuqin Song, Yan Hou:
Single-cell mosaic integration and cell state transfer with auto-scaling self-attention mechanism. - Sarah Samorodnitsky, Michael C. Wu:
Statistical analysis of multiple regions-of-interest in multiplexed spatial proteomics data. - Haoyang Zhang, Sha Liu, Bingxin Li, Xionghui Zhou:
IPFMC: an iterative pathway fusion approach for enhanced multi-omics clustering in cancer research. - Zhaohui Qin, Haoran Ren, Pei Zhao, Kaiyuan Wang, Huixia Liu, Chunbo Miao, Yanxiu Du, Junzhou Li, Liuji Wu, Zhen Chen:
Current computational tools for protein lysine acylation site prediction. - Yipeng Zhang, Cong Shen, Kelin Xia:
Multi-Cover Persistence (MCP)-based machine learning for polymer property prediction. - Dayu Hu, Renxiang Guan, Ke Liang, Hao Yu, Hao Quan, Yawei Zhao, Xinwang Liu, Kunlun He:
scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data. - Tongqing Wei, Chenqi Lu, Hanxiao Du, Qianru Yang, Xin Qi, Yankun Liu, Yi Zhang, Chen Chen, Yutong Li, Yuanhao Tang, Wen-Hong Zhang, Xu Tao, Ning Jiang:
DeepPBI-KG: a deep learning method for the prediction of phage-bacteria interactions based on key genes. - Yunwei Zhang, Samuel Müller:
Robust variable selection methods with Cox model - a selective practical benchmark study. - Matthijs Vynck, Wim Trypsteen, Olivier Thas, Jo Vandesompele, Ward De Spiegelaere:
Digital PCR threshold robustness analysis and optimization using dipcensR. - Harvard Wai Hann Hui, Weijia Kong, Wilson Wen Bin Goh:
Thinking points for effective batch correction on biomedical data. - Quang-Huy Nguyen, Ha Nguyen, Edwin C. Oh, Tin Nguyen:
Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review. - Tianyi Chen, Xindian Wei, Lianxin Xie, Yunfei Zhang, Cheng Liu, Wenjun Shen, Si Wu, Hau-San Wong:
SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction. - Jia Mi, Han Wang, Jing Li, Jinghong Sun, Chang Li, Jing Wan, Yuan Zeng, Jingyang Gao:
GGN-GO: geometric graph networks for predicting protein function by multi-scale structure features. - Zhenqiu Shu, Min Xia, Kaiwen Tan, Yongbing Zhang, Zhengtao Yu:
Multi-level multi-view network based on structural contrastive learning for scRNA-seq data clustering. - Rongzhuo Long, Ziyu Guo, Da Han, Boxiang Liu, Xudong Yuan, Guangyong Chen, Pheng-Ann Heng, Liang Zhang:
siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis. - Xiaoxiu Tan, Feng Xue, Chenhong Zhang, Tao Wang:
mbDriver: identifying driver microbes in microbial communities based on time-series microbiome data. - Qiule Yu, Zhixing Zhang, Guixia Liu, Weihua Li, Yun Tang:
ToxGIN: an In silico prediction model for peptide toxicity via graph isomorphism networks integrating peptide sequence and structure information. - Yetong Zhou, Shengming Zhou, Yue Bi, Quan Zou, Cangzhi Jia:
A two-task predictor for discovering phase separation proteins and their undergoing mechanism. - Wei Zhang, Yaxin Xu, Xiaoying Zheng, Juan Shen, Yuanyuan Li:
Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty. - Xueping Zhou, Manqi Cai, Molin Yue, Juan C. Celedón, Jiebiao Wang, Ying Ding, Wei Chen, Yanming Li:
Molecular group and correlation guided structural learning for multi-phenotype prediction. - Correction to: Structure prediction of linear and cyclic peptides using CABS-flex.
- Xiang Lin, Siqi Jiang, Le Gao, Zhi Wei, Junwen Wang:
MultiSC: a deep learning pipeline for analyzing multiomics single-cell data. - Qiang Su, Yi Long, Deming Gou, Junmin Quan, Qizhou Lian:
Enhancing RNA-seq analysis by addressing all co-existing biases using a self-benchmarking approach with 2D structural insights. - Xiuyu Jiang, Liqin Tan, Qingsong Zou:
DGCL: dual-graph neural networks contrastive learning for molecular property prediction. - Jing Chen, Ran Tao, Yi Qiu, Qun Yuan:
CMFHMDA: a prediction framework for human disease-microbe associations based on cross-domain matrix factorization. - Jiajing Xie, Yuhang Song, Hailong Zheng, Shijie Luo, Ying Chen, Chen Zhang, Rongshan Yu, Mengsha Tong:
PathMethy: an interpretable AI framework for cancer origin tracing based on DNA methylation. - Yun Zuo, Bangyi Zhang, Wenying He, Yue Bi, Xiangrong Liu, Xiangxiang Zeng, Zhaohong Deng:
MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization. - Chaorui Yan, Aoyun Geng, Zhuoyu Pan, Zilong Zhang, Feifei Cui:
MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides. - Wenwen Min, Zhiceng Shi, Jun Zhang, Jun Wan, Changmiao Wang:
Multimodal contrastive learning for spatial gene expression prediction using histology images. - Xin-Fei Wang, Lan Huang, Yan Wang, Ren-Chu Guan, Zhu-Hong You, Nan Sheng, Xuping Xie, Wenju Hou:
Multi-view learning framework for predicting unknown types of cancer markers via directed graph neural networks fitting regulatory networks. - Zhenhao Zhang, Yuxi Liu, Meichen Xiao, Kun Wang, Yu Huang, Jiang Bian, Ruolin Yang, Fuyi Li:
Graph contrastive learning as a versatile foundation for advanced scRNA-seq data analysis. - Gaoqi He, Shun Liu, Zhuoran Liu, Changbo Wang, Kai Zhang, Honglin Li:
Prototype-based contrastive substructure identification for molecular property prediction. - Liuyang Zhao, Landu Jiang, Yufeng Xie, Jianhao Huang, Haoran Xie, Jun Tian, Dian Zhang:
scDTL: enhancing single-cell RNA-seq imputation through deep transfer learning with bulk cell information. - Zhenhua Yu, Furui Liu, Yang Li:
scTCA: a hybrid Transformer-CNN architecture for imputation and denoising of scDNA-seq data. - Shunjie Chen, Pei Wang, Haiping Guo, Yujie Zhang:
Deciphering gene expression patterns using large-scale transcriptomic data and its applications. - David Seong, Samson Mataraso, Camilo Espinosa, Eloïse Berson, S. Momsen Reincke, Lei Xue, Chloe Kashiwagi, Yeasul Kim, Chi-Hung Shu, Philip Chung, Marc Ghanem, Feng Xie, Ronald J. Wong, Martin S. Angst, Brice Gaudilliere, Gary M. Shaw, David K. Stevenson, Nima Aghaeepour:
Generating pregnant patient biological profiles by deconvoluting clinical records with electronic health record foundation models. - Sascha Jung, Céline Barlier, Aitor Martinez Perez, Antonio del Sol:
Detecting expressed genes in cell populations at the single-cell level with scGeneXpress. - Siding Chen, Zhe Xu, Jinfeng Yin, Hongqiu Gu, Yanfeng Shi, Cang Guo, Xia Meng, Hao Li, Xinying Huang, Yong Jiang, Yongjun Wang:
Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study. - Miao Cui, Yadong Liu, Xian Yu, Hongzhe Guo, Tao Jiang, Yadong Wang, Bo Liu:
miniSNV: accurate and fast single nucleotide variant calling from nanopore sequencing data. - Ziyi Wang, Peng Luo, Mingming Xiao, Boyang Wang, Tianyu Liu, Xiangyu Sun:
Recover then aggregate: unified cross-modal deep clustering with global structural information for single-cell data. - Yidan Cui, Qingmin Lin, Xin Yuan, Fan Jiang, Shiyang Ma, Zhangsheng Yu:
Mediation analysis in longitudinal study with high-dimensional methylation mediators. - Zilin Ren, Jiarong Zhang, Yixiang Zhang, Tingting Yang, Pingping Sun, Jiguo Xue, Xiaochen Bo, Bo Zhou, Jiangwei Yan, Ming Ni:
NASTRA: accurate analysis of short tandem repeat markers by nanopore sequencing with repeat-structure-aware algorithm. - Chonghui Liu, Yan Zhang, Yingjian Liang, Tianjiao Zhang, Guohua Wang:
DrugReSC: targeting disease-critical cell subpopulations with single-cell transcriptomic data for drug repurposing in cancer. - Zachary D. Wallen, Mary K. Nesline, Sarabjot Pabla, Shuang Gao, Erik Vanroey, Stephanie B. Hastings, Heidi Ko, Kyle C. Strickland, Rebecca A Previs, Shengle Zhang, Jeffrey M. Conroy, Taylor J. Jensen, Elizabeth George, Marcia Eisenberg, Brian Caveney, Pratheesh Sathyan, Shakti Ramkissoon, Eric A Severson:
A consensus-based classification workflow to determine genetically inferred ancestry from comprehensive genomic profiling of patients with solid tumors. - Mengqiu Zheng, Shaofeng Lin, Kunqi Chen, Ruifeng Hu, Liming Wang, Zhongming Zhao, Haodong Xu:
MetaDegron: multimodal feature-integrated protein language model for predicting E3 ligase targeted degrons. - Qing Li, Zhihang Hu, Yixuan Wang, Lei Li, Yimin Fan, Irwin King, Gengjie Jia, Sheng Wang, Le Song, Yu Li:
Progress and opportunities of foundation models in bioinformatics. - Xun Zhang, Kun Qian, Hongwei Li:
Structure-preserved integration of scRNA-seq data using heterogeneous graph neural network. - Rongbo Shen, Meiling Cheng, Wencang Wang, Qi Fan, Huan Yan, Jiayue Wen, Zhiyuan Yuan, Jianhua Yao, Yixue Li, Jiao Yuan:
Graph domain adaptation-based framework for gene expression enhancement and cell type identification in large-scale spatially resolved transcriptomics. - Qi Dai, Hu Chen, Wen-Jing Yi, Jia-Ning Zhao, Wei Zhang, Ping-An He, Xiao-Qing Liu, Ying-Feng Zheng, Zhuoxing Shi:
Precision DNA methylation typing via hierarchical clustering of Nanopore current signals and attention-based neural network. - Qi Wang, Bolei Zhang, Yue Guo, Luyu Gong, Erguang Li, Jingping Yang:
Unlocking cross-modal interplay of single-cell joint profiling with CellMATE. - Hilbert Yuen In Lam, Jia Sheng Guan, Xing Er Ong, Robbe Pincket, Yuguang Mu:
Protein language models are performant in structure-free virtual screening. - Francesco Tabaro, Matthieu Boulard:
3t-seq: automatic gene expression analysis of single-copy genes, transposable elements, and tRNAs from RNA-seq data. - Yi Xie, Jianfei Yang, John F. Ouyang, Enrico Petretto:
scPanel: a tool for automatic identification of sparse gene panels for generalizable patient classification using scRNA-seq datasets. - Yuchuan Zhang, Zhikang Wang, Fang Ge, Xiaoyu Wang, Yiwen Zhang, Shanshan Li, Yuming Guo, Jiangning Song, Dong-Jun Yu:
MLSNet: a deep learning model for predicting transcription factor binding sites. - Junxin Li, Linbu Liao, Chao Zhang, Kaifang Huang, Pengfei Zhang, John Z. H. Zhang, Xiaochun Wan, Haiping Zhang:
Development and experimental validation of computational methods for human antibody affinity enhancement. - Zhen Wang, Yanhua Fang, Ruoyu Wang, Liwen Kong, Shanshan Liang, Shuai Tao:
Reconstructing tumor clonal heterogeneity and evolutionary relationships based on tumor DNA sequencing data. - Ali F. Alsulami:
Mut-Map: Comprehensive Computational Pipeline for Structural Mapping and Analysis of Cancer-Associated Mutations. - Jingkai Wang, Qiu-Wen Zhu, Jia-Hao Mai, Shun Zhang, Yuqing Wang, Jiatong Liang, Ji-Yuan Zhou:
A multi-omics study of brain tissue transcription and DNA methylation revealing the genetic pathogenesis of ADHD. - Mingjun Ji, Qing Yu, Xinzhuang Yang, Xianhong Yu, Jiaxin Wang, Chunfu Xiao, Ni A. An, Chuanhui Han, Chuan-Yun Li, Wanqiu Ding:
Long-range alternative splicing contributes to neoantigen specificity in glioblastoma. - Lin Yuan, Ling Zhao, Yufeng Jiang, Zhen Shen, Qinhu Zhang, Ming Zhang, Chun-Hou Zheng, De-Shuang Huang:
scMGATGRN: a multiview graph attention network-based method for inferring gene regulatory networks from single-cell transcriptomic data. - Muhammad A Nawaz, Igor E. Pamirsky, Kirill S. Golokhvast:
Bioinformatics in Russia: history and present-day landscape. - Xinwan Su, Chengyu Shi, Fangzhou Liu, Manman Tan, Ying Wang, Linyu Zhu, Yu Chen, Meng Yu, Xinyi Wang, Jian Liu, Yang Liu, Weiqiang Lin, Zhaoyuan Fang, Qiang Sun, Tianhua Zhou, Aifu Lin:
HMPA: a pioneering framework for the noncanonical peptidome from discovery to functional insights. - Yang Guo, Zhiqiang Xiao:
Constructing the dynamic transcriptional regulatory networks to identify phenotype-specific transcription regulators. - Jiabei Cheng, Xiaoyong Pan, Yi Fang, Kaiyuan Yang, Yiming Xue, Qingran Yan, Ye Yuan:
GexMolGen: cross-modal generation of hit-like molecules via large language model encoding of gene expression signatures. - Kathleen Noller, Patrick Cahan:
Cell cycle expression heterogeneity predicts degree of differentiation. - Xueying Xie, Lin Gui, Baixue Qiao, Guohua Wang, Shan Huang, Yuming Zhao, Shanwen Sun:
Deep learning in template-free de novo biosynthetic pathway design of natural products. - Han Phan, Céline Brouard, Raphaël Mourad:
Semi-supervised learning with pseudo-labeling compares favorably with large language models for regulatory sequence prediction. - Peihao Bai, Guanghui Li, Jiawei Luo, Cheng Liang:
Deep learning model for protein multi-label subcellular localization and function prediction based on multi-task collaborative training. - Sébastien De Landtsheer, Apurva Badkas, Dagmar Kulms, Thomas Sauter:
Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity. - Han-Ching Chan, Amrita Chattopadhyay, Tzu-Pin Lu:
Cross-population enhancement of PrediXcan predictions with a gnomAD-based east Asian reference framework. - Xiaoyu Li, Fangfang Zhu, Wenwen Min:
SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq. - Hailin Chen, Kuan Chen:
Predicting disease-associated microbes based on similarity fusion and deep learning. - Chen Su, William A Pastor, Amin Emad:
Deciphering lineage-relevant gene regulatory networks during endoderm formation by InPheRNo-ChIP.
Volume 25, Number Supplement 1, 2024
- Yufan Liu, Boxue Tian:
Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning. - Huishuang Tan, Zhixin Wang, Guang Hu:
GAABind: a geometry-aware attention-based network for accurate protein-ligand binding pose and binding affinity prediction. - Correction to: sciCNV: high-throughput paired profiling of transcriptomes and DNA copy number variations at single-cell resolution.
- Xiang Feng, Yu-Han Xiu, Hai-Xia Long, Zi-Tong Wang, Anas Bilal, Li-Ming Yang:
Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network. - Wei Peng, Zhichen He, Wei Dai, Wei Lan:
MHCLMDA: multihypergraph contrastive learning for miRNA-disease association prediction. - Xueqing Peng, Rong Gao, Jing Ren, Jianbo Lu, Xu Ma, Peiluan Li:
Specific network information gain for detecting the critical state of colorectal cancer based on gut microbiome. - Correction to: Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.
- Matsvei Tsishyn, Fabrizio Pucci, Marianne Rooman:
Quantification of biases in predictions of protein-protein binding affinity changes upon mutations. - Maryam Asadinezhad, Saeed Khoshnood, Parisa Asadollahi, Sobhan Ghafourian, Nourkhoda Sadeghifard, Iraj Pakzad, Yasaman Zeinivand, Nazanin Omidi, Ali Hematian, Behrooz Sadeghi Kalani:
Development of innovative multi-epitope mRNA vaccine against Pseudomonas aeruginosa using in silico approaches. - Jeffrey K. Weber, Joseph A. Morrone, Seung-gu Kang, Leili Zhang, Lijun Lang, Diego Chowell, Chirag Krishna, Tien Huynh, Prerana Parthasarathy, Binquan Luan, Tyler J. Alban, Wendy D. Cornell, Timothy A Chan:
Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity. - Dong Gao, Liping Ren, Yu-Duo Hao, Nalini Schaduangrat, Xiao-Wei Liu, Shi-Shi Yuan, Yu-He Yang, Yan Wang, Watshara Shoombuatong, Hui Ding:
The role of ncRNA regulatory mechanisms in diseases - case on gestational diabetes. - Qiang Li, Yu He, Jianbo Pan:
CrossFuse-XGBoost: accurate prediction of the maximum recommended daily dose through multi-feature fusion, cross-validation screening and extreme gradient boosting. - Song Zhai, Devan V. Mehrotra, Judong Shen:
Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities. - Rahaf M. Ahmad, Bassam R. Ali, Fatma Al Jasmi, Richard O. Sinnott, Noura Al Dhaheri, Mohd Saberi Mohamad:
A review of genetic variant databases and machine learning tools for predicting the pathogenicity of breast cancer. - Lavinia Morosi, Matteo Miotto, Sara Timo, Sara Carloni, Eleonora Bruno, Marina Meroni, Elisabetta Menna, Simona Lodato, Maria Rescigno, Giuseppe Martano:
MSIpixel: a fully automated pipeline for compound annotation and quantitation in mass spectrometry imaging experiments. - Komlan Atitey, Alison A. Motsinger-Reif, Benedict Anchang:
Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics. - Ying Li, Jing Gao, Xubin Zheng, Guole Nie, Jican Qin, Haiping Wang, Tao He, Åsa M. Wheelock, Chuan-Xing Li, Lixin Cheng, Xun Li:
Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model. - Yusen Lin, Yongjun Zhang, Hang Sun, Hang Jiang, Xing Zhao, Xiaojuan Teng, Jingxia Lin, Bowen Shu, Hao Sun, Yuhui Liao, Jiajian Zhou:
NanoDeep: a deep learning framework for nanopore adaptive sampling on microbial sequencing. - Yang Zhang, Caiqi Liu, Mujiexin Liu, Tianyuan Liu, Hao Lin, Cheng-Bing Huang, Lin Ning:
Attention is all you need: utilizing attention in AI-enabled drug discovery. - Ngoc-Quang Nguyen, Se-Jeong Park, Mogan Gim, Jaewoo Kang:
MulinforCPI: enhancing precision of compound-protein interaction prediction through novel perspectives on multi-level information integration. - Yipeng Wang, Pei Sun, Zicheng Zhao, Yousheng Yan, Wentao Yue, Kai Yang, Ruixia Liu, Hui Huang, Yinan Wang, Yin Chen, Nan Li, Hailong Feng, Jing Li, Yifan Liu, Yujiao Chen, Bairong Shen, Lijian Zhao, Chenghong Yin:
Identify gestational diabetes mellitus by deep learning model from cell-free DNA at the early gestation stage. - Shubo Tian, Qiao Jin, Lana Yeganova, Po-Ting Lai, Qingqing Zhu, Xiuying Chen, Yifan Yang, Qingyu Chen, Won Kim, Donald C. Comeau, Rezarta Islamaj, Aadit Kapoor, Xin Gao, Zhiyong Lu:
Opportunities and challenges for ChatGPT and large language models in biomedicine and health. - Jingru Wang, Yihang Xiao, Xuequn Shang, Jiajie Peng:
Predicting drug-target binding affinity with cross-scale graph contrastive learning. - Yan Zhao, Jun Yin, Li Zhang, Yong Zhang, Xing Chen:
Drug-drug interaction prediction: databases, web servers and computational models. - Yuran Jia, Junliang Liu, Li Chen, Tianyi Zhao, Yadong Wang:
THItoGene: a deep learning method for predicting spatial transcriptomics from histological images. - Haoquan Liu, Yiren Jian, Jinxuan Hou, Chen Zeng, Yunjie Zhao:
RNet: a network strategy to predict RNA binding preferences. - Jinzhao Li, Jiong Wang, Zhixiang Lin:
SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics. - Peng Zhang, Dingfan Zhang, Wuai Zhou, Lan Wang, Boyang Wang, Tingyu Zhang, Shao Li:
Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine. - Wei Wang, Gaolin Yuan, Shitong Wan, Ziwei Zheng, Dong Liu, Hongjun Zhang, Juntao Li, Yun Zhou, Xianfang Wang:
A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs. - Zhenjiang Fan, Jie Sun, Henry Thorpe, Stephen Lee, Soyeon Kim, Hyun Jung Park:
Deep neural network learning biological condition information refines gene-expression-based cell subtypes. - Ning Zhang, Fengxia Yang, Pengfei Zhao, Nana Jin, Haonan Wu, Tao Liu, Qingshan Geng, Xiaojun Yang, Lixin Cheng:
MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients. - Tianyue Wang, Langcheng Wang, Xujun Zhang, Chao Shen, Odin Zhang, Jike Wang, Jialu Wu, Ruofan Jin, Donghao Zhou, Shicheng Chen, Liwei Liu, Xiaorui Wang, Chang-Yu Hsieh, Guangyong Chen, Peichen Pan, Yu Kang, Tingjun Hou:
Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency. - Zhaoyu Fang, Teng Liu, Ruiqing Zheng, Jin A, Mingzhu Yin, Min Li:
stAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics. - Le Li, Chenyang Hong, Jie Xu, Claire Yik-Lok Chung, Alden King-Yung Leung, Delbert Almerick T. Boncan, Lixin Cheng, Kwok-Wai Lo, Paul B. S. Lai, John Wong, Jingying Zhou, Alfred Sze-Lok Cheng, Ting-Fung Chan, Feng Yue, Kevin Y. Yip:
Accurate identification of structural variations from cancer samples. - Yu Liu:
methylClass: an R package to construct DNA methylation-based classification models. - Lihong Peng, Liangliang Huang, Qiongli Su, Geng Tian, Min Chen, Guosheng Han:
LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine. - Petr Popov, Roman Kalinin, Pavel Buslaev, Igor Kozlovskii, Mark Zaretckii, Dmitry Karlov, Alexander Gabibov, Alexey Stepanov:
Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites. - Qun Ren, Ning Qu, Jingjing Sun, Jingyi Zhou, Jin Liu, Lin Ni, Xiaochu Tong, Zimei Zhang, Xiangtai Kong, Yiming Wen, Yitian Wang, Dingyan Wang, Xiaomin Luo, Sulin Zhang, Mingyue Zheng, Xutong Li:
KinomeMETA: meta-learning enhanced kinome-wide polypharmacology profiling. - Mingyang Shi, Chizu Tanikawa, Hans Markus Munter, Masato Akiyama, Satoshi Koyama, Kohei Tomizuka, Koichi Matsuda, Gregory Mark Lathrop, Chikashi Terao, Masaru Koido, Yoichiro Kamatani:
Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels. - Yansen Su, Zhiyang Hu, Fei Wang, Yannan Bin, Chun-Hou Zheng, Haitao Li, Haowen Chen, Xiangxiang Zeng:
AMGDTI: drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network. - Congzhou M. Sha, Jian Wang, Nikolay V. Dokholyan:
Differentiable rotamer sampling with molecular force fields. - Yun Xia, Yan Gao, Ming-Yu Liu, Lei Li, Wen Pan, Ling-Zi Mao, Zhongzheng Yang, Mei Yang, An-Yuan Guo:
ICBcomb: a comprehensive expression database for immune checkpoint blockade combination therapy. - Keyi Li, Xiaoyang Chen, Shuang Song, Lin Hou, Shengquan Chen, Rui Jiang:
Cofea: correlation-based feature selection for single-cell chromatin accessibility data. - Yang Liu, Hanlin Li, Tianyu Zeng, Yang Wang, Hongqi Zhang, Ying Wan, Zheng Shi, Renzhi Cao, Hua Tang:
Integrated bulk and single-cell transcriptomes reveal pyroptotic signature in prognosis and therapeutic options of hepatocellular carcinoma by combining deep learning. - Jin Seok Lee, Dhuvarakesh Karthikeyan, Misha Fini, Benjamin G. Vincent, Alex Rubinsteyn:
ACE configurator for ELISpot: optimizing combinatorial design of pooled ELISpot assays with an epitope similarity model. - Xiaofeng Xu, Ying Li, Taoyu Chen, Chao Hou, Liang Yang, Peiyu Zhu, Yi Zhang, Tingting Li:
VIPpred: a novel model for predicting variant impact on phosphorylation events driving carcinogenesis. - Zhecheng Zhou, Linlin Zhuo, Xiangzheng Fu, Quan Zou:
Joint deep autoencoder and subgraph augmentation for inferring microbial responses to drugs. - Ken Nakatsu, Mayumi Jijiwa, Vedbar Khadka, Masaki Nasu, Youping Deng:
sRNAfrag: a pipeline and suite of tools to analyze fragmentation in small RNA sequencing data. - Xiao Liang, Lijie Cao, Hao Chen, Lidan Wang, Yangyun Wang, Lijuan Fu, Xiaqin Tan, Enxiang Chen, Yubin Ding, Jing Tang:
A critical assessment of clustering algorithms to improve cell clustering and identification in single-cell transcriptome study. - Shuang Wang, Bochen Shen, Lanting Guo, Mengqi Shang, Jinze Liu, Qi Sun, Bairong Shen:
scFed: federated learning for cell type classification with scRNA-seq. - Kayra Kosoglu, Zeynep Aydin, Nurcan Tuncbag, Attila Gürsoy, Ozlem Keskin:
Structural coverage of the human interactome. - Yizhong Wang, Yang Li, Cankun Wang, Chan-Wang Jerry Lio, Qin Ma, Bingqiang Liu:
CEMIG: prediction of the cis-regulatory motif using the de Bruijn graph from ATAC-seq. - Jiasheng Wang, Ying-Wooi Wan, Rami Al-Ouran, Meichen Huang, Zhandong Liu:
CoRegNet: unraveling gene co-regulation networks from public RNA-Seq repositories using a beta-binomial statistical model. - Nhat Truong Pham, Rajan Rakkiyapan, Jongsun Park, Adeel Malik, Balachandran Manavalan:
H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA. - Jiani Ma, Jiangning Song, Neil D. Young, Bill C. H. Chang, Pasi K. Korhonen, Tulio L. Campos, Hui Liu, Robin B. Gasser:
'Bingo' - a large language model- and graph neural network-based workflow for the prediction of essential genes from protein data. - Tianjiao Zhang, Ziheng Zhang, Liangyu Li, Benzhi Dong, Guohua Wang, Dandan Zhang:
GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data. - Ren-Gang Zhang, Hong-Yun Shang, Kai-Hua Jia, Yong-Peng Ma:
Subgenome phasing for complex allopolyploidy: case-based benchmarking and recommendations. - Chuan-Xing Li, Hongyan Chen, Nazanin Zounemat Kermani, Ian M. Adcock, C. Magnus Sköld, Meng Zhou, Åsa M. Wheelock:
Consensus clustering with missing labels (ccml): a consensus clustering tool for multi-omics integrative prediction in cohorts with unequal sample coverage. - Emily McLeish, Nataliya Slater, Frank L. Mastaglia, Merrilee Needham, Jerome D. Coudert:
From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies. - Jian-Hua Pan, Pu-Feng Du:
SilenceREIN: seeking silencers on anchors of chromatin loops by deep graph neural networks. - Sizhe Qiu, Simiao Zhao, Aidong Yang:
DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates. - Xingyu Chen, Min Deng, Zihan Wang, Chen Huang:
MMP3C: anin-silicoframework to depict cancer metabolic plasticity using gene expression profiles. - Jin Liu, Weixing Zeng, Shichao Kan, Min Li, Ruiqing Zheng:
CAKE: a flexible self-supervised framework for enhancing cell visualization, clustering and rare cell identification. - Alex C. H. Wong, Justin J.-L. Wong, John E. J. Rasko, Ulf Schmitz:
SpliceWiz: interactive analysis and visualization of alternative splicing in R. - Philipp A. Toussaint, Florian Leiser, Scott Thiebes, Matthias Schlesner, Benedikt Brors, Ali Sunyaev:
Explainable artificial intelligence for omics data: a systematic mapping study. - Yujia Qin, Angela Maggio, Dale Hawkins, Laura Beaudry, Allen Kim, Daniel Pan, Ting Gong, Yuanyuan Fu, Hua Yang, Youping Deng:
Whole-genome bisulfite sequencing data analysis learning module on Google Cloud Platform. - Alan E. Woessner, Usman Anjum, Hadi Salman, Jacob Lear, Jeffrey T. Turner, Ross Campbell, Laura Beaudry, Justin Zhan, Lawrence E. Cornett, Susan Gauch, Kyle P. Quinn:
Identifying and training deep learning neural networks on biomedical-related datasets. - Etienne Z. Gnimpieba, Timothy W. Hartman, Tuyen Do, Jessica Zylla, Shiva Aryal, Samuel J. Haas, Diing D. M. Agany, Bichar Dip Shrestha Gurung, Valena Doe, Zelaikha B. Yosufzai, Daniel Pan, Ross Campbell, Victor C. Huber, Rajesh Kumar Sani, Venkataramana Gadhamshetty, Carol Lushbough:
Biofilm marker discovery with cloud-based dockerized metagenomics analysis of microbial communities. - Kyle A. O'Connell, Benjamin Kopchick, Thad B. Carlson, David Belardo, Stephanie D. Byrum:
Understanding proteome quantification in an interactive learning module on Google Cloud Platform. - Avinash M. Veerappa, M. Jordan Rowley, Angela Maggio, Laura Beaudry, Dale Hawkins, Allen Kim, Sahil Sethi, Paul L. Sorgen, Chittibabu Guda:
CloudATAC: a cloud-based framework for ATAC-Seq data analysis. - Christopher L. Hemme, Laura Beaudry, Zelaikha B. Yosufzai, Allen Kim, Daniel Pan, Ross Campbell, Marcia Price, Bongsup P. Cho:
A cloud-based learning module for biomarker discovery. - Owen M. Wilkins, Ross Campbell, Zelaikha B. Yosufzai, Valena Doe, Shannon M. Soucy:
Cloud-based introduction to BASH programming for biologists. - Nathan A. Ruprecht, Joshua D. Kennedy, Benu Bansal, Sonalika Singhal, Donald Sens, Angela Maggio, Valena Doe, Dale Hawkins, Ross Campbel, Kyle A. O'Connell, Jappreet Singh Gill, Kalli Schaefer, Sandeep K. Singhal:
Transcriptomics and epigenetic data integration learning module on Google Cloud. - Ha Nguyen, Van-Dung Pham, Hung Nguyen, Bang Tran, Juli Petereit, Tin Nguyen:
CCPA: cloud-based, self-learning modules for consensus pathway analysis using GO, KEGG and Reactome. - Ming Lei, Lakshmi K. Matukumalli, Krishan Arora, Nick Weber, Rachel Malashock, Fenglou Mao, Susan Gregurick, Jon R. Lorsch:
NIGMS Sandbox: a learning platform toward democratizing cloud computing for biomedical research. - Wenkang Wang, Yunyan Shuai, Qiurong Yang, Fuhao Zhang, Min Zeng, Min Li:
A comprehensive computational benchmark for evaluating deep learning-based protein function prediction approaches. - Shengjie Sun, Georgialina Rodriguez, Gaoshu Zhao, Jason E. Sanchez, Wenhan Guo, Dan Du, Omar J. Rodriguez Moncivais, Dehua Hu, Jing Liu, Robert Arthur Kirken, Lin Li:
A novel approach to study multi-domain motions in JAK1's activation mechanism based on energy landscape. - Dayu Tan, Cheng Yang, Jing Wang, Yansen Su, Chunhou Zheng:
scAMAC: self-supervised clustering of scRNA-seq data based on adaptive multi-scale autoencoder. - Lanying Wang, Yuxuan Hu, Lin Gao:
Adjustment of scRNA-seq data to improve cell-type decomposition of spatial transcriptomics. - Lei Hou, Zhi Geng, Zhongshang Yuan, Xu Shi, Chuan Wang, Feng Chen, Hongkai Li, Fuzhong Xue:
MRSL: a causal network pruning algorithm based on GWAS summary data. - Shaowei An, Miaoshan Lu, Ruimin Wang, Jinyin Wang, Hengxuan Jiang, Cong Xie, Junjie Tong, Changbin Yu:
Ion entropy and accurate entropy-based FDR estimation in metabolomics. - Chuyu Pan, Bolun Cheng, Xiaoyue Qin, Shiqiang Cheng, Li Liu, Xuena Yang, Peilin Meng, Na Zhang, Dan He, Qingqing Cai, Wenming Wei, Jingni Hui, Yan Wen, Yumeng Jia, Huan Liu, Feng Zhang:
Enhanced polygenic risk score incorporating gene-environment interaction suggests the association of major depressive disorder with cardiac and lung function. - Jiacai Yi, Zi-Yi Yang, Wen-Tao Zhao, Zhi-Jiang Yang, Xiao-Chen Zhang, Chengkun Wu, Ai-Ping Lu, Dong-Sheng Cao:
ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization. - Yitao Lu, Qian Chu, Zhen Li, Mengdi Wang, Robert A. Gatenby, Qingpeng Zhang:
Deep reinforcement learning identifies personalized intermittent androgen deprivation therapy for prostate cancer. - Jiating Yu, Jiacheng Leng, Zhichao Hou, Duanchen Sun, Ling-Yun Wu:
Incorporating network diffusion and peak location information for better single-cell ATAC-seq data analysis. - Hui Wan, Musu Yuan, Yiwei Fu, Minghua Deng:
Continually adapting pre-trained language model to universal annotation of single-cell RNA-seq data. - Yueyi Cai, Shunfang Wang:
Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping. - José Córdoba-Caballero, James Richard Perkins, Federico García-Criado, Diana Gallego, Alicia Navarro-Sánchez, Mireia Moreno-Estellés, Concepción Garcés, Fernando Bonet, Carlos Romá-Mateo, Rocio Toro, Belén Perez, Pascual Sanz, Matthias Kohl, Elena Rojano, Pedro Seoane, Juan A. G. Ranea:
Exploring miRNA-target gene pair detection in disease with coRmiT. - Sapir Israeli, Yoram Louzoun:
Single-residue linear and conformational B cell epitopes prediction using random and ESM-2 based projections. - Huadong Xing, Pengli Cai, Dongliang Liu, Mengying Han, Juan Liu, Yingying Le, Dachuan Zhang, Qian-Nan Hu:
High-throughput prediction of enzyme promiscuity based on substrate-product pairs. - Zhang Wei, Chenjun Wu, Xing Feiyang, Mingfeng Jiang, Zhang Yixuan, Liu Qi, Zhuoxing Shi, Dai Qi:
scHybridBERT: integrating gene regulation and cell graph for spatiotemporal dynamics in single-cell clustering. - Ziying Zhang, Yaxian Cai, Biao Zhang, Wei Zheng, Peter L. Freddolino, Guijun Zhang, Xiaogen Zhou:
DEMO-EM2: assembling protein complex structures from cryo-EM maps through intertwined chain and domain fitting. - Hongru Shen, Jilei Liu, Kexin Chen, Xiangchun Li:
Language model enables end-to-end accurate detection of cancer from cell-free DNA. - Krithika Bhuvaneshwar, Yuriy Gusev:
Translational bioinformatics and data science for biomarker discovery in mental health: an analytical review. - Clement Bernard, Guillaume Postic, Sahar Ghannay, Fariza Tahi:
RNAdvisor: a comprehensive benchmarking tool for the measure and prediction of RNA structural model quality. - Wenjing Li, Bin Wang, Jin Dai, Yan Kou, Xiaojun Chen, Yi Pan, Shuangwei Hu, Zhenjiang Zech Xu:
Partial order relation-based gene ontology embedding improves protein function prediction. - Md. Toufikuzzaman, Md. Abul Hassan Samee, M. Sohel Rahman:
CRISPR-DIPOFF: an interpretable deep learning approach for CRISPR Cas-9 off-target prediction. - Shanjun Mao, Xiao Huang, Runjiu Chen, Chenyang Zhang, Yizhu Diao, Zongjin Li, Qingzhe Wang, Shan Tang, Shuixia Guo:
STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data. - Yuxuan Chen, Ruiqing Zheng, Jin Liu, Min Li:
scMLC: an accurate and robust multiplex community detection method for single-cell multi-omics data. - Correction to: labelSeg: segment annotation for tumor copy number alteration profiles.
- Fei Xu, Huan Hu, Hai Lin, Jun Lu, Feng Cheng, Jiqian Zhang, Xiang Li, Jianwei Shuai:
scGIR: deciphering cellular heterogeneity via gene ranking in single-cell weighted gene correlation networks. - Majid Mokhtari, Samane Khoshbakht, Kobra Ziyaei, Mohammad Esmaeil Akbari, Sayyed Sajjad Moravveji:
New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics. - Istvan Grexa, Zsanett Zsófia Iván, Ede Migh, Ferenc Kovács, Hella A Bolck, Xiang Zheng, Andreas Mund, Nikita Moshkov, Vivien Miczán, Krisztian Koos, Péter Horváth:
SuperCUT, an unsupervised multimodal image registration with deep learning for biomedical microscopy. - Miriam Zuckerbrot-Schuldenfrei, Sarit Aviel-Ronen, Alona Zilberberg, Sol Efroni:
Ovarian cancer is detectable from peripheral blood using machine learning over T-cell receptor repertoires. - Ze-Gang Wei, Xiao-Dan Zhang, Xing-Guo Fan, Yu Qian, Fei Liu, Fang-Xiang Wu:
pathMap: a path-based mapping tool for long noisy reads with high sensitivity. - Correction to: JCcirc: circRNA full-length sequence assembly through integrated junction contigs.
- Dongmei Han, Yurun Li, Linxiao Wang, Xuan Liang, Yuanyuan Miao, Wenran Li, Sijia Wang, Zhen Wang:
Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data. - Bing Wang, Xiangzheng Zhang, Xudong Han, Bingjie Hao, Yan Li, Xuejiang Guo:
TransGCN: a semi-supervised graph convolution network-based framework to infer protein translocations in spatio-temporal proteomics. - Rui Xu, Huan Zhang, Michael W. Crowder, Jiangjiang Zhu:
Multiple and Optimal Screening Subset: a method selecting global characteristic congeners for robust foodomics analysis. - Mengyao Wang, Shuai Cheng Li, Bairong Shen:
Elevated incidence of somatic mutations at prevalent genetic sites. - Correction to: Privacy-aware estimation of relatedness in admixed populations.
- Lun Hu, Menglong Zhang, Pengwei Hu, Jun Zhang, Chao Niu, Xueying Lu, Xiangrui Jiang, Yupeng Ma:
Dual-channel hypergraph convolutional network for predicting herb-disease associations. - Kun Wang, Jiangshan Shi, Xiaochu Tong, Ning Qu, Xiangtai Kong, Shengkun Ni, Jing Xing, Xutong Li, Mingyue Zheng:
TG468: a text graph convolutional network for predicting clinical response to immune checkpoint inhibitor therapy. - Dayu Hu, Ke Liang, Zhibin Dong, Jun Wang, Yawei Zhao, Kunlun He:
Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data. - Yufang Zhang, Yanyi Chu, Shenggeng Lin, Yi Xiong, Dong-Qing Wei:
ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler. - Juyeon Kim, Sung-Hye Park, Hyunju Lee:
PANCDR: precise medicine prediction using an adversarial network for cancer drug response. - Roxana Zahedi, Reza Ghamsari, Ahmadreza Argha, Callum Macphillamy, Amin Beheshti, Roohallah Alizadehsani, Nigel H. Lovell, Mohammad Lotfollahi, Hamid Alinejad-Rokny:
Deep learning in spatially resolved transcriptomics: a comprehensive technical view. - Carlos Reding, Naphat Satapoomin, Matthew B. Avison:
Hound: a novel tool for automated mapping of genotype to phenotype in bacterial genomes assembled de novo. - Correction to: A benchmark study on current GWAS models in admixed populations.
- Tong Liu, Qin-Tian Qiu, Kang-Jian Hua, Bin-Guang Ma:
Chromosome structure modeling tools and their evaluation in bacteria. - Zitian Yang, Zexin Zhang, Jing Li, Wen Chen, Changning Liu:
CRISPRlnc: a machine learning method for lncRNA-specific single-guide RNA design of CRISPR/Cas9 system. - Hailiang Song, Qin Zhang, Hongxia Hu:
polyGBLUP: a modified genomic best linear unbiased prediction improved the genomic prediction efficiency for autopolyploid species. - Yi-Xuan Xiong, Xiao-Fei Zhang:
scDOT: enhancing single-cell RNA-Seq data annotation and uncovering novel cell types through multi-reference integration. - Zhendong Zhang, Tao Jiang, Gaoyang Li, Shuqi Cao, Yadong Liu, Bo Liu, Yadong Wang:
Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data. - Shufang Wu, Tao Feng, Waijiao Tang, Cancan Qi, Jie Gao, Xiaolong He, Jiaxuan Wang, Hongwei Zhou, Zhencheng Fang:
metaProbiotics: a tool for mining probiotic from metagenomic binning data based on a language model. - Yao Zhang, Pengyu Zhang, Hao Wu:
Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers. - Mythili Srinivasan, Apeksha Gangurde, Ashwini Y. Chandane, Amol Tagalpallewar, Anil Pawar, Akshay M. Baheti:
Integrating network pharmacology and in silico analysis deciphers Withaferin-A's anti-breast cancer potential via hedgehog pathway and target network interplay. - Chengyuan Yang, Himansu Kumar, Pora Kim:
FusionNW, a potential clinical impact assessment of kinases in pan-cancer fusion gene network. - Ye-Hong Yang, Jun-Tao Yang, Jiang-Feng Liu:
Lactylation prediction models based on protein sequence and structural feature fusion. - Chuxi Xiao, Yixin Chen, Qiuchen Meng, Lei Wei, Xuegong Zhang:
Benchmarking multi-omics integration algorithms across single-cell RNA and ATAC data. - Nan Zhao, Bingqing Han, Cuicui Zhao, Jinbo Xu, Xinqi Gong:
ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking. - Qi Xu, Imee M. A del Mundo, Maha Zewail-Foote, Brian T. Luke, Karen M. Vasquez, Jeanne Kowalski:
MoCoLo: a testing framework for motif co-localization. - Davide Buzzao, Miguel Castresana Aguirre, Dimitri Guala, Erik L. L. Sonnhammer:
Benchmarking enrichment analysis methods with the disease pathway network. - Felipe Campelo, Francisco Pereira Lobo:
The rise of taxon-specific epitope predictors. - Mohammad Shahbazy, Sri H. Ramarathinam, Chen Li, Patricia T. Illing, Pouya Faridi, Nathan P. Croft, Anthony W. Purcell:
MHCpLogics: an interactive machine learning-based tool for unsupervised data visualization and cluster analysis of immunopeptidomes. - Faiza Mehmood, Shazia Arshad, Muhammad Shoaib:
ADH-Enhancer: an attention-based deep hybrid framework for enhancer identification and strength prediction. - Hongyan Du, Guo-Wei Wei, Tingjun Hou:
Multiscale topology in interactomic network: from transcriptome to antiaddiction drug repurposing. - Rui Wang, Tao Wang, Linlin Zhuo, Jinhang Wei, Xiangzheng Fu, Quan Zou, Xiaojun Yao:
Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization. - Ziheng Duan, Siwei Xu, Shushrruth Sai Srinivasan, Ahyeon Hwang, Cheyu Lee, Feng Yue, Mark Gerstein, Yu Luan, Matthew J. Girgenti, Jing Zhang:
scENCORE: leveraging single-cell epigenetic data to predict chromatin conformation using graph embedding. - Chongyue Zhao, Zhongli Xu, Xinjun Wang, Shiyue Tao, William A Macdonald, Kun He, Amanda C. Poholek, Kong Chen, Heng Huang, Wei Chen:
Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression. - Yuhao Tao, Xiaoang Sun, Fei Wang:
BiGATAE: a bipartite graph attention auto-encoder enhancing spatial domain identification from single-slice to multi-slices. - Huiwen Wang:
Prediction of protein-ligand binding affinity via deep learning models. - Correction to: From data to diagnosis: how machine learning is revolutionizing biomarker discovery in idiopathic inflammatory myopathies.
- Surendra Kumar, Mahesh K. Teli, Mi-Hyun Kim:
GPCR-IPL score: multilevel featurization of GPCR-ligand interaction patterns and prediction of ligand functions from selectivity to biased activation. - Yahang Liu, Qian Gao, Kecheng Wei, Chen Huang, Ce Wang, Yongfu Yu, Guoyou Qin, Tong Wang:
High-dimensional generalized median adaptive lasso with application to omics data. - Xiangren Kong, Lihong Diao, Peng Jiang, Shiyan Nie, Shuzhen Guo, Dong Li:
DDK-Linker: a network-based strategy identifies disease signals by linking high-throughput omics datasets to disease knowledge. - Lu-Xiang Guo, Lei Wang, Zhu-Hong You, Chang-Qing Yu, Meng-Lei Hu, Bo-Wei Zhao, Yang Li:
Biolinguistic graph fusion model for circRNA-miRNA association prediction. - Allan Ringeval, Sarah Farhat, Alexander Fedosov, Marco Gerdol, Samuele Greco, Lou Mary, Maria Vittoria Modica, Nicolas Puillandre:
DeTox: a pipeline for the detection of toxins in venomous organisms. - Judith Bernett, David B. Blumenthal, Markus List:
Cracking the black box of deep sequence-based protein-protein interaction prediction.
Volume 25, Number 3, 2024
- Rosario Vitale, Leandro A Bugnon, Emilio Fenoy, Diego H. Milone, Georgina Stegmayer:
Evaluating large language models for annotating proteins. - Sikta Das Adhikari, Yuehua Cui, Jianrong Wang:
BayesKAT: bayesian optimal kernel-based test for genetic association studies reveals joint genetic effects in complex diseases. - Yuxuan Zong, Hongyu Zhao, Tao Wang:
mbDecoda: a debiased approach to compositional data analysis for microbiome surveys. - Correction to: Addressing barriers in comprehensiveness, accessibility, reusability, interoperability and reproducibility of computational models in systems biology.
- Chenhao Zhang, Chengyun Zhang, Tianfeng Shang, Ning Zhu, Xinyi Wu, Hongliang Duan:
HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints. - Xiaowen Hu, Pan Zhang, Dayun Liu, Jiaxuan Zhang, Yuanpeng Zhang, Yihan Dong, Yanhao Fan, Lei Deng:
IGCNSDA: unraveling disease-associated snoRNAs with an interpretable graph convolutional network. - Gourab Saha, Shashata Sawmya, Arpita Saha, Md Ajwad Akil, Sadia Tasnim, Md Saifur Rahman, M. Sohel Rahman:
PRIEST: predicting viral mutations with immune escape capability of SARS-CoV-2 using temporal evolutionary information. - Xiaoru Dong, Jack R. Leary, Chuanhao Yang, Maigan Brusko, Todd M. Brusko, Rhonda Bacher:
Data-driven selection of analysis decisions in single-cell RNA-seq trajectory inference. - Yanhao Fan, Che Zhang, Xiaowen Hu, Zhijian Huang, Jiameng Xue, Lei Deng:
SGCLDGA: unveiling drug-gene associations through simple graph contrastive learning. - Yixiang Huang, Hao Jiang, Wai-Ki Ching:
scEWE: high-order element-wise weighted ensemble clustering for heterogeneity analysis of single-cell RNA-sequencing data. - Niranjan Kumar, Rakesh Srivastava:
Deep learning in structural bioinformatics: current applications and future perspectives. - Yulong Shi, Chongwu Li, Xinben Zhang, Cheng Peng, Peng Sun, Qian Zhang, Leilei Wu, Ying Ding, Dong Xie, Zhijian Xu, Weiliang Zhu:
D3EGFR: a webserver for deep learning-guided drug sensitivity prediction and drug response information retrieval for EGFR mutation-driven lung cancer. - Partha Pratim Ray:
Timely need for navigating the potential and downsides of LLMs in healthcare and biomedicine. - Ayelet Peres, Vered Klein, Boaz Frankel, William D. Lees, Pazit Polak, Mark Meehan, Artur Rocha, João Correia Lopes, Gur Yaari:
Guidelines for reproducible analysis of adaptive immune receptor repertoire sequencing data. - Tianjiao Zhang, Zhenao Wu, Liangyu Li, Jixiang Ren, Ziheng Zhang, Guohua Wang:
CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data. - Jie Pan, Zhen Zhang, Ying Li, Jiaoyang Yu, Zhuhong You, Chenyu Li, Shixu Wang, Minghui Zhu, Fengzhi Ren, Xuexia Zhang, Yanmei Sun, Shiwei Wang:
A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts. - Jiahui Guan, Lantian Yao, Peilin Xie, Chia-Ru Chung, Yixian Huang, Ying-Chih Chiang, Tzong-Yi Lee:
A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy. - Yushan Qiu, Dong Guo, Pu Zhao, Quan Zou:
scMNMF: a novel method for single-cell multi-omics clustering based on matrix factorization. - Vikas Singh, Nikhil Kirtipal, Byeongsop Song, Sunjae Lee:
Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference. - Peng Wu, Huabin Du, Yingchao Yan, Tzong-Yi Lee, Chen Bai, Song Wu:
Guided diffusion for molecular generation with interaction prompt. - Adi Jabarin, Guy Shtar, Valeria Feinshtein, Eyal Mazuz, Bracha Shapira, Shimon Ben-Shabat, Lior Rokach:
Eravacycline, an antibacterial drug, repurposed for pancreatic cancer therapy: insights from a molecular-based deep learning model. - Kaixin Hu, Fernando Meyer, Zhi-Luo Deng, Ehsaneddin Asgari, Tzu-Hao Kuo, Philipp C. Münch, Alice C. McHardy:
Assessing computational predictions of antimicrobial resistance phenotypes from microbial genomes. - Kun Wu, Xiulong Yang, Zixu Wang, Na Li, Jialu Zhang, Lizhuang Liu:
Data-balanced transformer for accelerated ionizable lipid nanoparticles screening in mRNA delivery. - Lingkuan Meng, Xingjian Chen, Ke Cheng, Nanjun Chen, Zetian Zheng, Fuzhou Wang, Hongyan Sun, Ka-Chun Wong:
TransPTM: a transformer-based model for non-histone acetylation site prediction. - Xin Wei, Huagang Tan, Briallen Lobb, William Zhen, Zijing Wu, Donovan H. Parks, Josh D. Neufeld, Gabriel Moreno-Hagelsieb, Andrew C. Doxey:
AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods. - Yiming Li, Haoling Zhang, Yuxin Chen, Yue Shen, Zhi Ping:
DNA Bloom Filter enables anti-contamination and file version control for DNA-based data storage. - William J. Thrift, Jason Perera, Sivan Cohen, Nicolas W. Lounsbury, Hem R. Gurung, Christopher M. Rose, Jieming Chen, Suchit Jhunjhunwala, Kai Liu:
Graph-pMHC: graph neural network approach to MHC class II peptide presentation and antibody immunogenicity. - Jason Chen, Ayisha Zia, Albert Luo, Hanze Meng, Fengbin Wang, Jie Hou, Renzhi Cao, Dong Si:
Enhancing cryo-EM structure prediction with DeepTracer and AlphaFold2 integration. - Wanchun Nie, Tianyi Qiu, Yiwen Wei, Hao Ding, Zhixiang Guo, Jingxuan Qiu:
Advances in phage-host interaction prediction: in silico method enhances the development of phage therapies. - Yicheng Huang, Ziyuan Wang, Monica A Schmidt, Handong Su, Lizhong Xiong, Jianwei Zhang:
DEGAP: Dynamic elongation of a genome assembly path. - Yinghong Li, Yinqi Yang, Zhuohao Tong, Yu Wang, Qin Mi, Mingze Bai, Guizhao Liang, Bo Li, Kunxian Shu:
A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods. - Kumar Selvarajoo, Sebastian Maurer-Stroh:
Towards multi-omics synthetic data integration. - Correction to: A new paradigm for applying deep learning to protein-ligand interaction prediction.
- Yicheng Lin, Jiakang Ma, Haozhe Yuan, Ziqiang Chen, Xingyu Xu, Mengping Jiang, Jialiang Zhu, Weida Meng, Wenqing Qiu, Yun Liu:
Integrating Reinforcement Learning and Monte Carlo Tree Search for enhanced neoantigen vaccine design. - Kai Wang, Xuan Zeng, Jingwen Zhou, Fei Liu, Xiaoli Luan, Xinglong Wang:
BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning. - Fu V. Song, Jiaqi Su, Sixing Huang, Neng Zhang, Kaiyue Li, Ming Ni, Maofu Liao:
DeepSS2GO: protein function prediction from secondary structure. - Lai Heng Tan, Chee Keong Kwoh, Yuguang Mu:
RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method. - Yan-Feng Hong, Hongquan Xu, Yuhong Liu, Sisi Zhu, Chao Tian, Gong-Xing Chen, Feng Zhu, Lin Tao:
DDID: a comprehensive resource for visualization and analysis of diet-drug interactions. - Lei Liu, Liu Yang, Suqi Cao, Zhigang Gao, Bin Yang, Guoqing Zhang, Ruixin Zhu, Dingfeng Wu:
CyclicPepedia: a knowledge base of natural and synthetic cyclic peptides. - Chengxin Li, Shuang Shuang Xie, Jiaqi Wang, Septavera Sharvia, Kuan Yoow Chan:
SC-Track: a robust cell-tracking algorithm for generating accurate single-cell lineages from diverse cell segmentations. - Tianyi Jiang, Zeyu Wang, Wenchao Yu, Jinhuan Wang, Shanqing Yu, Xiaoze Bao, Bin Wei, Qi Xuan:
Mix-Key: graph mixup with key structures for molecular property prediction. - Tao Wang, Zhen Li, Linlin Zhuo, Yifan Chen, Xiangzheng Fu, Quan Zou:
MS-BACL: enhancing metabolic stability prediction through bond graph augmentation and contrastive learning. - Byungjo Lee, Dongkwan Shin:
Contrastive learning for enhancing feature extraction in anticancer peptides. - Jasmine Barra, Federico Taverna, Fabian Bong, Ibrahim Ahmed, Tobias K. Karakach:
Error modelled gene expression analysis (EMOGEA) provides a superior overview of time course RNA-seq measurements and low count gene expression. - Md Alamin, Most Humaira Sultana, Isaac Adeyemi Babarinde, A. K. M. Azad, Mohammad Ali Moni, Haiming Xu:
Single-cell RNA-seq data analysis reveals functionally relevant biomarkers of early brain development and their regulatory footprints in human embryonic stem cells (hESCs). - Yuan Zhou, Zhen Chen, Mengjie Yang, Fengyun Chen, Jiayi Yin, Yintao Zhang, Xuheng Zhou, Xiuna Sun, Ziheng Ni, Lu Chen, Qun Lv, Feng Zhu, Shuiping Liu:
FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response. - Hongxi Yan, Dawei Weng, Dongguo Li, Yu Gu, Wenji Ma, Qingjie Liu:
Prior knowledge-guided multilevel graph neural network for tumor risk prediction and interpretation via multi-omics data integration. - Wei Lan, Haibo Liao, Qingfeng Chen, Lingzhi Zhu, Yi Pan, Yi-Ping Phoebe Chen:
DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery. - Maryam Kafikang, Chamudi Abeysiriwardana, Vikash K. Singh, Chan Young Koh, Janet Prichard, Sunil K. Mor, Abdeltawab M. Hendawi:
Analysis of Emerging Variants of Turkey Reovirus using Machine Learning. - Guang Yang, Jianing Li, Jinlu Hu, Jian-Yu Shi:
Recognition of cyanobacteria promoters via Siamese network-based contrastive learning under novel non-promoter generation. - Shubo Tian, Qiao Jin, Zhiyong Lu:
Response to Letter to Editor 'Timely need for navigating the potential and downsides of LLMs in healthcare and biomedicine'. - Junseok Lee, Sukwon Yun, Yeongmin Kim, Tianlong Chen, Manolis Kellis, Chanyoung Park:
Single-cell RNA sequencing data imputation using bi-level feature propagation. - Junchao Zhu, Hao Dai, Luonan Chen:
Revealing cell-cell communication pathways with their spatially coupled gene programs. - Qingyuan Yang, Wanmei Song, Hanizaier Reheman, Dan Wang, Jieming Qu, Yanan Li:
PANoptosis, an indicator of COVID-19 severity and outcomes. - Linyuan Guo, Jianxin Wang:
GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking. - Wenchuan Zhao, Yufeng Yu, Guosheng Liu, Yanchun Liang, Dong Xu, Xiaoyue Feng, Renchu Guan:
MSI-DTI: predicting drug-target interaction based on multi-source information and multi-head self-attention. - Ali Kishk, Maria Pires Pacheco, Tony Heurtaux, Thomas Sauter:
Metabolic models predict fotemustine and the combination of eflornithine/rifamycin and adapalene/cannabidiol for the treatment of gliomas. - Huiyang Yu, Chunmei Shi, Weiming He, Feng Li, Bo Ouyang:
PanDepth, an ultrafast and efficient genomic tool for coverage calculation. - Trang Nguyen, Anthony Campbell, Ankit Kumar, Edwin Amponsah, Madalina Fiterau, Leili Shahriyari:
Optimal fusion of genotype and drug embeddings in predicting cancer drug response. - Jana Schwarzerova, Martin Hurta, Vojtech Barton, Matej Lexa, Dirk Walther, Valentine Provazník, Wolfram Weckwerth:
A perspective on genetic and polygenic risk scores - advances and limitations and overview of associated tools. - Zhongxiao Li, Antonella Napolitano, Monica Fedele, Xin Gao, Francesco Napolitano:
AI identifies potent inducers of breast cancer stem cell differentiation based on adversarial learning from gene expression data. - Sepehr Ashrafzadeh, Geoffrey Brian Golding, Silvana Ilie, Lucian Ilie:
Scoring alignments by embedding vector similarity. - Yuyao Zhai, Liang Chen, Minghua Deng:
scBOL: a universal cell type identification framework for single-cell and spatial transcriptomics data. - Seyed Amir Malekpour, Laleh Haghverdi, Mehdi Sadeghi:
Single-cell multi-omics analysis identifies context-specific gene regulatory gates and mechanisms. - Chuanyang Zheng, Yixuan Wang, Yuqi Cheng, Xuesong Wang, Hongxin Wei, Irwin King, Yu Li:
scNovel: a scalable deep learning-based network for novel rare cell discovery in single-cell transcriptomics. - Yao Chen, Ward De Spiegelaere, Wim Trypsteen, David Gleerup, Jo Vandesompele, Antoon Lievens, Matthijs Vynck, Olivier Thas:
Benchmarking digital PCR partition classification methods with empirical and simulated duplex data. - Ruijie Liu, Yuanpeng Zhang, Qi Wang, Xiaoping Zhang:
TransAC4C - a novel interpretable architecture for multi-species identification of N4-acetylcytidine sites in RNA with single-base resolution. - Kipper Fletez-Brant, Yunjiang Qiu, David U. Gorkin, Ming Hu, Kasper D. Hansen:
Removing unwanted variation between samples in Hi-C experiments. - Furui Liu, Fangyuan Shi, Fang Du, Xiangmei Cao, Zhenhua Yu:
CoT: a transformer-based method for inferring tumor clonal copy number substructure from scDNA-seq data. - Hongchen Ji, Xiang-Xu Wang, Qiong Zhang, Chengkai Zhang, Hong-Mei Zhang:
Predicting TCR sequences for unseen antigen epitopes using structural and sequence features. - Yuexing Liu, Yao Luo, Xin Lu, Hao Gao, Ruikun He, Xin Zhang, Xuguang Zhang, Yixue Li:
Genotypic-phenotypic landscape computation based on first principle and deep learning. - Xue Zhang, Jingcheng Wu, Yuanyuan Luo, Yilin Wang, Yujie Wu, Xiaobin Xu, Yufang Zhang, Ruiying Kong, Ying Chi, Yisheng Sun, Shuqing Chen, Qiaojun He, Feng Zhu, Zhan Zhou:
CovEpiAb: a comprehensive database and analysis resource for immune epitopes and antibodies of human coronaviruses. - Maísa R. Ferro dos Santos, Edoardo Giuili, Andries De Koker, Celine Everaert, Katleen De Preter:
Computational deconvolution of DNA methylation data from mixed DNA samples. - Guihong Wan, Zoltan Maliga, Boshen Yan, Tuulia Vallius, Yingxiao Shi, Sara Khattab, Crystal Chang, Ajit Johnson Nirmal, Kun-Hsing Yu, David S. L. Wei, Christine G. Lian, Mia S. Desimone, Peter K. Sorger, Yevgeniy R. Semenov:
SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data. - Kexin Li, Hong Yang, Anqi Lin, Jiayi Xie, Haitao Wang, Jianguo Zhou, Shamus R. Carr, Zaoqu Liu, Xiaohua Li, Jian Zhang, Quan Cheng, David S. Schrump, Peng Luo, Ting Wei:
CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity. - Pengzhen Jia, Fuhao Zhang, Chaojin Wu, Min Li:
A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond. - Yuchen Shi, Jian Wan, Xin Zhang, Tingting Liang, Yuyu Yin:
scCRT: a contrastive-based dimensionality reduction model for scRNA-seq trajectory inference. - Shan Gao, Tao Wang, Zhifa Han, Yang Hu, Ping Zhu, Yanli Xue, Chen Huang, Yan Chen, Guiyou Liu:
Interpretation of 10 years of Alzheimer's disease genetic findings in the perspective of statistical heterogeneity. - Aria Hassanali Aragh, Pegah Givehchian, Razieh Moslemi Amirani, Raziyeh Masumshah, Changiz Eslahchi:
MiRAGE: mining relationships for advanced generative evaluation in drug repositioning. - Xiaorong Tan, Qianhui Liu, Yanpeng Fang, Sen Yang, Fei Chen, Jianmin Wang, Defang Ouyang, Jie Dong, Wenbin Zeng:
Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs. - Elias Farr, Daniel Dimitrov, Christina Schmidt, Dénes Türei, Sebastian Lobentanzer, Aurélien Dugourd, Julio Saez-Rodriguez:
MetalinksDB: a flexible and contextualizable resource of metabolite-protein interactions. - Rui Jiang, Wentao Huang, Xinqi Qiu, Jianyi Chen, Ruibang Luo, Ruijie Zeng, Shuangshuang Tong, Yanlin Lyu, Panpan Sun, Qizhou Lian, Felix W. Leung, Yufeng Liu, Weihong Sha, Hao Chen:
Unveiling promising drug targets for autism spectrum disorder: insights from genetics, transcriptomics, and proteomics. - Yi-Fang Chuang, Kang-Chen Fan, Yin-Yuan Su, Ming-Fong Wu, Yen-Ling Chiu, Yi-Chien Liu, Chen-Ching Lin:
Precision probiotics supplement strategy in aging population based on gut microbiome composition. - Chengxin Zhang, Lydia Freddolino:
A large-scale assessment of sequence database search tools for homology-based protein function prediction. - Gal Passi, Sari Lieberman, Fouad Zahdeh, Omer Murik, Paul Renbaum, Rachel Beeri, Michal Linial, Dalit May, Ephrat Levy-Lahad, Dina Schneidman-Duhovny:
Discovering predisposing genes for hereditary breast cancer using deep learning. - Yitian Fang, Mingshuang Luo, Zhixiang Ren, Leyi Wei, Dong-Qing Wei:
CELA-MFP: a contrast-enhanced and label-adaptive framework for multi-functional therapeutic peptides prediction. - Sheng Wang, Emily K. Stroup, Ting-You Wang, Rendong Yang, Zhe Ji:
Comparative analyses of gene networks mediating cancer metastatic potentials across lineage types. - Iori Azuma, Tadahaya Mizuno, Hiroyuki Kusuhara:
GLDADec: marker-gene guided LDA modeling for bulk gene expression deconvolution. - Minghan Li, Yuqing Su, Yanbo Gao, Weidong Tian:
ReCIDE: robust estimation of cell type proportions by integrating single-reference-based deconvolutions. - Musaddiq K. Lodi, Muzammil Lodi, Kezie Osei, Vaishnavi Ranganathan, Priscilla Hwang, Preetam Ghosh:
CHAI: consensus clustering through similarity matrix integration for cell-type identification. - Zhengwei Li, Lipeng Wan, Lei Wang, Wenjing Wang, Ru Nie:
HHOMR: a hybrid high-order moment residual model for miRNA-disease association prediction. - Sunghong Park, Changhyung Hong, Sang Joon Son, Hyunwoong Roh, Doyoon Kim, Hyunjung Shin, Hyun Goo Woo:
Identification of molecular subtypes of dementia by using blood-proteins interaction-aware graph propagational network. - Shuangge Ma:
Editorial. - Matteo Pallocca, Martina Betti, Sara Baldinelli, Ramona Palombo, Gabriele Bucci, Luca Mazzarella, Giovanni Tonon, Gennaro Ciliberto:
Clinical bioinformatics desiderata for molecular tumor boards. - Yuan Zhou, Pei Geng, Shan Zhang, Feifei Xiao, Guoshuai Cai, Li Chen, Qing Lu:
Multimodal functional deep learning for multiomics data. - Hamed Meshkin, Joel Zirkle, Ghazal Arabi Darreh Dor, Anik Chaturbedi, Shilpa Chakravartula, John Mann, Bradlee Thrasher, Zhihua Li:
Harnessing large language models' zero-shot and few-shot learning capabilities for regulatory research. - Pengbo Duan, Kuo Yang, Xin Su, Shuyue Fan, Xin Dong, Fenghui Zhang, Xianan Li, Xiaoyan Xing, Qiang Zhu, Jian Yu, Xuezhong Zhou:
HTINet2: herb-target prediction via knowledge graph embedding and residual-like graph neural network. - Jian Zhao, Zhewei Chen, Meng Zhang, Lingxiao Zou, Shan He, Jingjing Liu, Quan Wang, Xiaofeng Song, Jing Wu:
DeepIRES: a hybrid deep learning model for accurate identification of internal ribosome entry sites in cellular and viral mRNAs. - Jilei Liu, Hongru Shen, Kexin Chen, Xiangchun Li:
Large language model produces high accurate diagnosis of cancer from end-motif profiles of cell-free DNA. - Tong Wang, Guangming Xiang, Siwei He, Liyun Su, Yuguang Wang, Xuefeng Yan, Hongzhong Lu:
DeepEnzyme: a robust deep learning model for improved enzyme turnover number prediction by utilizing features of protein 3D-structures. - Qing Wang, Yuzhou Feng, Yanfei Wang, Bo Li, Jianguo Wen, Xiaobo Zhou, Qianqian Song:
AntiFormer: graph enhanced large language model for binding affinity prediction. - Yann Spöri, Jean-François Flot:
Haxe as a Swiss knife for bioinformatic applications: the SeqPHASE case story. - Rama Rajadnya, Nidhi Sharma, Akanksha Mahajan, Amrita Ulhe, Rajesh Patil, Mahabaleshwar Hegde, Aniket Mali:
Novel systems biology experimental pipeline reveals matairesinol's antimetastatic potential in prostate cancer: an integrated approach of network pharmacology, bioinformatics, and experimental validation. - Qian Wang, Xiaotong Hu, Zhiqiang Wei, Hao Lu, Hao Liu:
Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides. - Xin Liu, Yu Pang, Junqi Shan, Yunfei Wang, Yanhua Zheng, Yuhang Xue, Xuerong Zhou, Wenjun Wang, Yanlai Sun, Xiaojing Yan, Jiantao Shi, Xiaoxue Wang, Hongcang Gu, Fan Zhang:
Beyond the base pairs: comparative genome-wide DNA methylation profiling across sequencing technologies. - Jin-Wook Choi, Jin-Ok Lee, Sejoon Lee:
Detecting microsatellite instability by length comparison of microsatellites in the 3′ untranslated region with RNA-seq. - Min Zhang, Qi Cheng, Zhenyu Wei, Jiayu Xu, Shiwei Wu, Nan Xu, Chengkui Zhao, Lei Yu, Weixing Feng:
BertTCR: a Bert-based deep learning framework for predicting cancer-related immune status based on T cell receptor repertoire. - Tianjian Liang, Ze-Yu Sun, Margaret G. Hines, Kerri Jo Penrose, Yixuan Hao, Xiaojie Chu, John W. Mellors, Dimiter S. Dimitrov, Xiang-Qun Xie, Wei Li, Zhiwei Feng:
AI-based IsAb2.0 for antibody design. - Jens Uwe Loers, Vanessa Vermeirssen:
A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data. - Daniel A Acheampong, Piroon Jenjaroenpun, Thidathip Wongsurawat, Alongkorn Kurilung, Yotsawat Pomyen, Sangam Kandel, Pattapon Kunadirek, Natthaya Chuaypen, Kanthida Kusonmano, Intawat Nookaew:
CAIM: coverage-based analysis for identification of microbiome. - Na Yuan, Peilin Jia:
Comprehensive assessment of long-read sequencing platforms and calling algorithms for detection of copy number variation. - Arno van Hilten, Sonja Katz, Edoardo Saccenti, Wiro J. Niessen, Gennady V. Roshchupkin:
Designing interpretable deep learning applications for functional genomics: a quantitative analysis. - Lingmin Zhan, Yingdong Wang, Aoyi Wang, Yuanyuan Zhang, Caiping Cheng, Jinzhong Zhao, Wuxia Zhang, Jianxin Chen, Peng Li:
A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks. - Joseph Szymborski, Amin Emad:
INTREPPPID - an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction. - Min Zhang, Wensheng Zhang, Xiaoke Ma:
ST-SCSR: identifying spatial domains in spatial transcriptomics data via structure correlation and self-representation. - Muslum Kaan Arici, Nurcan Tuncbag:
Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction. - Weifang Liu, Wujuan Zhong, Paola Giusti-Rodríguez, Zhiyun Jiang, Geoffery W. Wang, Huaigu Sun, Ming Hu, Yun Li:
SnapHiC-G: identifying long-range enhancer-promoter interactions from single-cell Hi-C data via a global background model. - Linjia Kang, Songhua Zhou, Shuyan Fang, Shichao Liu:
Adapting differential molecular representation with hierarchical prompts for multi-label property prediction. - Zichang Xu, Hendra S. Ismanto, Dianita S. Saputri, Soichiro Haruna, Guanqun Sun, Jan Wilamowski, Shunsuke Teraguchi, Ayan Sengupta, Songling Li, Daron M. Standley:
Robust detection of infectious disease, autoimmunity, and cancer from the paratope networks of adaptive immune receptors. - Xiaoyu Wang, Fuyi Li, Yiwen Zhang, Seiya Imoto, Hsin-Hui Shen, Shanshan Li, Yuming Guo, Jian Yang, Jiangning Song:
Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects. - Ao Shen, Mingzhi Yuan, Yingfan Ma, Jie Du, Manning Wang:
PGBind: pocket-guided explicit attention learning for protein-ligand docking. - Xin Bai, Hui-Cong Yao, Bo Wu, Luo-Ran Liu, Yu-Ying Ding, Chuan-Le Xiao:
DeepBAM: a high-accuracy single-molecule CpG methylation detection tool for Oxford nanopore sequencing. - Or Shkuri, Sapir Israeli, Yuli Tshuva, Martin Maiers, Yoram Louzoun:
Efficient test for deviation from Hardy-Weinberg equilibrium with known or ambiguous typing in highly polymorphic loci. - Haitao Yang, Xin Wang, Zechen Zhang, Fuzhao Chen, Hongyan Cao, Lina Yan, Xia Gao, Hui Dong, Yuehua Cui:
A high-dimensional omnibus test for set-based association analysis. - Lantian Yao, Peilin Xie, Jiahui Guan, Chia-Ru Chung, Wenyang Zhang, Junyang Deng, Yixian Huang, Ying-Chih Chiang, Tzong-Yi Lee:
ACP-CapsPred: an explainable computational framework for identification and functional prediction of anticancer peptides based on capsule network. - Jinyeon Jo, Nayoung Ha, Yunmi Ji, Ahra Do, Je Hyun Seo, Bumjo Oh, Sungkyoung Choi, Eun Kyung Choe, Woojoo Lee, Jang Won Son, Sungho Won:
Genetic determinants of obesity in Korean populations: exploring genome-wide associations and polygenic risk scores. - Tongxuan Lv, Yong Zhang, Junlin Liu, Qiang Kang, Lin Liu:
Multi-omics integration for both single-cell and spatially resolved data based on dual-path graph attention auto-encoder. - Jianye Yang, Haitao Fu, Fei-Yang Xue, Menglu Li, Yuyang Wu, Zhanhui Yu, Haohui Luo, Jing Gong, Xiaohui Niu, Wen Zhang:
Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms. - Kechen Fan, Mengfan Li, Jiarong Zhang, Zihan Xie, Daguang Jiang, Xiaochen Bo, Dongsheng Zhao, Shenghui Shi, Ming Ni:
ReadCurrent: a VDCNN-based tool for fast and accurate nanopore selective sequencing. - Renhua Song, Gavin J. Sutton, Fuyi Li, Qian Liu, Justin J.-L. Wong:
Variable calling of m6A and associated features in databases: a guide for end-users. - Haoyang Mi, Shamilene Sivagnanam, Won Jin Ho, Shuming Zhang, Daniel Bergman, Atul Deshpande, Alexander S. Baras, Elizabeth M. Jaffee, Lisa M. Coussens, Elana J. Fertig, Aleksander S. Popel:
Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology. - Yuxuan Si, Zihan Huang, Zhengqing Fang, Zhouhang Yuan, Zhengxing Huang, Yingming Li, Ying Wei, Fei Wu, Yu-Feng Yao:
Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks. - Xuechun Zhang, Xiaoxuan Hu, Tongtong Zhang, Ling Yang, Chunhong Liu, Ning Xu, Haoyi Wang, Wen Sun:
PLM_Sol: predicting protein solubility by benchmarking multiple protein language models with the updated Escherichia coli protein solubility dataset. - Bencong Zhu, Zhen Zhang, Suet Yi Leung, Xiaodan Fan:
NetMIM: network-based multi-omics integration with block missingness for biomarker selection and disease outcome prediction. - Xuanjin Cheng, Murathan T. Goktas, Laura M. Williamson, Martin Krzywinski, David T. Mulder, Lucas Swanson, Jill Slind, Jelena Sihvonen, Cynthia R. Chow, Amy Carr, Ian Bosdet, Tracy Tucker, Sean Young, Richard A. Moore, Karen L. Mungall, Stephen Yip, Steven J. M. Jones:
Enhancing clinical genomic accuracy with panelGC: a novel metric and tool for quantifying and monitoring GC biases in hybridization capture panel sequencing. - Yifan Guo, Haoyu Wen, Zongwei Chen, Mengxia Jiao, Yuchen Zhang, Di Ge, Ronghua Liu, Jie Gu:
Conjoint analysis of succinylome and phosphorylome reveals imbalanced HDAC phosphorylation-driven succinylayion dynamic contibutes to lung cancer. - Jiaying Zhao, Wai-Ki Ching, Chi-Wing Wong, Xiaoqing Cheng:
BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell imputation. - Jianan Li, Keisuke Yanagisawa, Yutaka Akiyama:
CycPeptMP: enhancing membrane permeability prediction of cyclic peptides with multi-level molecular features and data augmentation. - Shuangshuang Cheng, Zhilin Ning, Ke Huang, Yuan Yuan, Xinjiang Tan, Yuwen Pan, Rui Zhang, Lei Tian, Yan Lu, Xiaoji Wang, Dongsheng Lu, Yajun Yang, Yaqun Guan, Dolikun Mamatyusupu, Shuhua Xu:
Analysis of sex-biased gene expression in a Eurasian admixed population. - Namhee Kim, Jonghoon Lee, Jongwan Kim, Yunseong Kim, Kwang-Hyun Cho:
Canalizing kernel for cell fate determination. - Ben Cao, Kun Wang, Lei Xie, Jianxia Zhang, Yunzhu Zhao, Bin Wang, Pan Zheng:
PELMI: Realize robust DNA image storage under general errors via parity encoding and local mean iteration. - Kunjie Fan, Birkan Gokbag, Shan Tang, Shangjia Li, Yirui Huang, Lingling Wang, Lijun Cheng, Lang Li:
Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction. - Runyu Miao, Danlin Liu, Liyun Mao, Xingyu Chen, Leihao Zhang, Zhen Yuan, Shanshan Shi, Honglin Li, Shiliang Li:
GR-pKa: a message-passing neural network with retention mechanism for pKa prediction. - Varuni Sarwal, Seungmo Lee, Jianzhi Yang, Sriram Sankararaman, Mark Chaisson, Eleazar Eskin, Serghei Mangul:
VISTA: an integrated framework for structural variant discovery. - Hirotsugu Shiroma, Youssef Darzi, Etsuko Terajima, Zenichi Nakagawa, Hirotaka Tsuchikura, Naoki Tsukuda, Yuki Moriya, Shujiro Okuda, Susumu Goto, Takuji Yamada:
Enteropathway: the metabolic pathway database for the human gut microbiota. - Xi Zhou, Jing Yang, Yin Luo, Xiao Shen:
HNCGAT: a method for predicting plant metabolite-protein interaction using heterogeneous neighbor contrastive graph attention network. - Wenying Li, Haiyun Wang, Jianping Zhao, Junfeng Xia, Xiaoqiang Sun:
scHyper: reconstructing cell-cell communication through hypergraph neural networks. - Meisam Yousefi, Wayne Ren See, Kam Leng Aw-Yong, Wai Suet Lee, Cythia Lingli Yong, Felic Fanusi, Gavin J. D. Smith, Eng Eong Ooi, Shang Li, Sujoy Ghosh, Yaw Shin Ooi:
GeneRaMeN enables integration, comparison, and meta-analysis of multiple ranked gene lists to identify consensus, unique, and correlated genes. - Liuyin Chen, Sanyuan Fu, Zijun Zhang:
CMTT-JTracker: a fully test-time adaptive framework serving automated cell lineage construction. - Chelsea A Matthews, Nathan S. Watson-Haigh, Rachel A. Burton, Anna E. Sheppard:
A gentle introduction to pangenomics. - Fathima Ridha, M. Michael Gromiha:
MPA-MutPred: a novel strategy for accurately predicting the binding affinity change upon mutation in membrane protein complexes. - Jiajun Xu, Yujia Gao, Quan Lu, Renyi Zhang, Jianfeng Gui, Xiaoshuang Liu, Zhenyu Yue:
RiceSNP-BST: a deep learning framework for predicting biotic stress-associated SNPs in rice. - Ilya Mazein, Adrien Rougny, Alexander Mazein, Ron Henkel, Lea Gütebier, Lea Michaelis, Marek Ostaszewski, Reinhard Schneider, Venkata P. Satagopam, Lars Juhl Jensen, Dagmar Waltemath, Judith A. H. Wodke, Irina Balaur:
Graph databases in systems biology: a systematic review. - Tingting Hou, Xiaoxi Shen, Shan Zhang, Muxuan Liang, Li Chen, Qing Lu:
AIGen: an artificial intelligence software for complex genetic data analysis. - Yang Xu, Dian Lv, Xuanxuan Zou, Liang Wu, Xun Xu, Xin Zhao:
BFAST: joint dimension reduction and spatial clustering with Bayesian factor analysis for zero-inflated spatial transcriptomics data. - Congcong Jia, Tong Wang, Dingtong Cui, Yaxin Tian, Gaiqin Liu, Zhaoyang Xu, Yanhong Luo, Ruiling Fang, Hongmei Yu, Yanbo Zhang, Yuehua Cui, Hongyan Cao:
A metagene based similarity network fusion approach for multi-omics data integration identified novel subtypes in renal cell carcinoma. - Chien Lee, Pei-Hua Wang, Yufeng Jane Tseng:
Digital annealing optimization for natural product structure elucidation. - Neng Wang, Zhenyao Ye, Tianzhou Ma:
TIPS: a novel pathway-guided joint model for transcriptome-wide association studies. - Yourui Han, Bolin Chen, Zhongwen Bi, Jun Bian, Ruiming Kang, Xuequn Shang:
The mathematical exploration for the mechanism of lung adenocarcinoma formation and progression. - Sejin Park, Hyunju Lee:
Robust self-supervised learning strategy to tackle the inherent sparsity in single-cell RNA-seq data. - Martin Bosilj, Alen Suljic, Samo Zakotnik, Jan Slunecko, Rok Kogoj, Misa Korva:
MetaAll: integrative bioinformatics workflow for analysing clinical metagenomic data. - Yu-Qing Geng, Fei-Liao Lai, Hao Luo, Feng Gao:
Nmix: a hybrid deep learning model for precise prediction of 2'-O-methylation sites based on multi-feature fusion and ensemble learning. - Martin Grasshoff, Milena Kalmer, Nicolas Chatain, Kim Kricheldorf, Angela Maurer, Ralf Weiskirchen, Steffen Koschmieder, Ivan G. Costa:
SIngle cell level Genotyping Using scRna Data (SIGURD).
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