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ORCIDBriefings in Bioinformatics, Volume 24
Volume 24, Number 1, January 2023
- Jiung-Wen Chen, Lisa Shrestha, George Green, André Leier
, Tatiana T. Marquez-Lago:
The hitchhikers' guide to RNA sequencing and functional analysis.
- Jiacheng Lin, Lijun Wu, Jinhua Zhu, Xiaobo Liang, Yingce Xia, Shufang Xie, Tao Qin, Tie-Yan Liu:
R2-DDI: relation-aware feature refinement for drug-drug interaction prediction. - Jing Wang, Junfeng Xia, Haiyun Wang, Yansen Su, Chun-Hou Zheng:
scDCCA: deep contrastive clustering for single-cell RNA-seq data based on auto-encoder network. - Eva María Trinidad, Enrique Vidal, Esther Coronado, Anna Esteve-Codina, Victoria Castel, Adela Cañete Nieto, Marta Gut, Simon Heath, Jaime Font de Mora:
Liquidhope: methylome and genomic profiling from very limited quantities of plasma-derived DNA. - Correction to: siGCD: a web server to explore survival interaction of genes, cells and drugs in human cancers.
- Yue-Hua Feng, Shao-Wu Zhang, Yi-Yang Feng, Qing-Qing Zhang, Ming-Hui Shi, Jian-Yu Shi:
A social theory-enhanced graph representation learning framework for multitask prediction of drug-drug interactions. - Xiayang Li, Moxuan Chen, Huaming Wu:
Multiple errors correction for position-limited DNA sequences with GC balance and no homopolymer for DNA-based data storage. - Chengqian Lu, Lishen Zhang, Min Zeng, Wei Lan, Guihua Duan, Jianxin Wang:
Inferring disease-associated circRNAs by multi-source aggregation based on heterogeneous graph neural network. - Ziyang Tang, Tonglin Zhang, Baijian Yang, Jing Su, Qianqian Song:
spaCI: deciphering spatial cellular communications through adaptive graph model. - Bailing Zhou, Maolin Ding, Jing Feng, Baohua Ji, Pingping Huang, Junye Zhang, Xue Yu, Zanxia Cao, Yuedong Yang, Yaoqi Zhou, Jihua Wang:
EVlncRNA-Dpred: improved prediction of experimentally validated lncRNAs by deep learning. - Yimeng Wang, Mengting Huang, Hua Deng, Weihua Li, Zengrui Wu, Yun Tang, Guixia Liu:
Identification of vital chemical information via visualization of graph neural networks. - Ilias Moutsopoulos, Eleanor C. Williams, Irina Mohorianu:
bulkAnalyseR: an accessible, interactive pipeline for analysing and sharing bulk multi-modal sequencing data. - Yuanyuan Chen, Hao Zhang, Xiao Sun:
Improving the performance of single-cell RNA-seq data mining based on relative expression orderings. - Ryley Dorney, Bijay P. Dhungel, John E. J. Rasko, Lionel Hebbard, Ulf Schmitz:
Recent advances in cancer fusion transcript detection. - Lei Wang, Shao-Hua Shi, Hui Li, Xiangxiang Zeng, Su-You Liu, Zhao-Qian Liu, Yafeng Deng, Ai-Ping Lu, Tingjun Hou, Dong-Sheng Cao:
Reducing false positive rate of docking-based virtual screening by active learning. - Khandakar Tanvir Ahmed, Sze Cheng, Qian Li, Jeongsik Yong, Wei Zhang:
Incomplete time-series gene expression in integrative study for islet autoimmunity prediction. - Denis Beslic, Georg Tscheuschner, Bernhard Y. Renard, Michael G. Weller, Thilo Muth:
Comprehensive evaluation of peptide de novo sequencing tools for monoclonal antibody assembly. - Jun Liu, Kai-Long Zhao, Guijun Zhang:
Improved model quality assessment using sequence and structural information by enhanced deep neural networks. - Wenjie Du, Xiaoting Yang, Di Wu, Fenfen Ma, Baicheng Zhang, Chaochao Bao, Yaoyuan Huo, Jun Jiang, Xin Chen, Yang Wang:
Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers. - Jiayin Zhou, Wen Song, Qichao Tu:
To assemble or not to assemble: metagenomic profiling of microbially mediated biogeochemical pathways in complex communities. - Wayland Yeung, Zhongliang Zhou, Liju Mathew, Nathan Gravel, Rahil Taujale, Brady O'boyle, Mariah Salcedo, Aarya Venkat, William Lanzilotta, Sheng Li, Natarajan Kannan:
Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies. - Wayland Yeung, Zhongliang Zhou, Sheng Li, Natarajan Kannan:
Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings. - Vandana Bharti, Shabari S. Nair, Akshat Jain, Kaushal Kumar Shukla, Bhaskar Biswas:
Concept drift detection in toxicology datasets using discriminative subgraph-based drift detector. - Fang Ge, Chen Li, Shahid Iqbal, Muhammad Arif, Fuyi Li, Maha A. Thafar, Zihao Yan, Apilak Worachartcheewan, Xiaofeng Xu, Jiangning Song, Dong-Jun Yu:
VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants. - Kai Yu, Zekun Liu, Haoyang Cheng, Shihua Li, Qingfeng Zhang, Jia Liu, Huai-Qiang Ju, Zhixiang Zuo, Qi Zhao, Shiyang Kang, Zexian Liu:
dSCOPE: a software to detect sequences critical for liquid-liquid phase separation. - Xing Chen, Li Huang:
Computational model for disease research. - Yanan Tian, Xiaorui Wang, Xiaojun Yao, Huanxiang Liu, Ying Yang:
Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism. - Danni Hong, Hongli Lin, Lifang Liu, Muya Shu, Jianwu Dai, Falong Lu, Mengsha Tong, Jialiang Huang:
Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis. - Lishen Zhang, Chengqian Lu, Min Zeng, Yaohang Li, Jianxin Wang:
CRMSS: predicting circRNA-RBP binding sites based on multi-scale characterizing sequence and structure features. - Zheng Zhang, Shengquan Chen, Zhixiang Lin:
RefTM: reference-guided topic modeling of single-cell chromatin accessibility data. - Sayed-Rzgar Hosseini, Xiaobo Zhou:
CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy. - Kai Zheng, Xin-Lu Zhang, Lei Wang, Zhu-Hong You, Bo-Ya Ji, Xiao Liang, Zheng-Wei Li:
SPRDA: a link prediction approach based on the structural perturbation to infer disease-associated Piwi-interacting RNAs. - Wenchuang He, Kunli Xiang, Caijin Chen, Jie Wang, Zhiqiang Wu:
Master graph: an essential integrated assembly model for the plant mitogenome based on a graph-based framework. - Nishant Kumar, Sumeet Patiyal, Shubham Choudhury, Ritu Tomer, Anjali Dhall, Gajendra P. S. Raghava:
DMPPred: a tool for identification of antigenic regions responsible for inducing type 1 diabetes mellitus. - Ke Han, Jianchun Wang, Yu Wang, Lei Zhang, Mengyao Yu, Fang Xie, Dequan Zheng, Yaoqun Xu, Yijie Ding, Jie Wan:
A review of methods for predicting DNA N6-methyladenine sites. - Yishu Wang, Juan Qi, Dongmei Ai:
DPADM: a novel algorithm for detecting drug-pathway associations based on high-throughput transcriptional response to compounds. - Xue Li, Peifu Han, Wenqi Chen, Changnan Gao, Shuang Wang, Tao Song, Muyuan Niu, Alfonso Rodríguez-Patón:
MARPPI: boosting prediction of protein-protein interactions with multi-scale architecture residual network. - Bo Song, Hao Li, Mengyun Jiang, Zhongtian Gao, Suikang Wang, Lei Gao, Yunsheng Chen, Wujiao Li:
slORFfinder: a tool to detect open reading frames resulting from trans-splicing of spliced leader sequences. - Hai Yang, Lipeng Gan, Rui Chen, Dongdong Li, Jing Zhang, Zhe Wang:
From multi-omics data to the cancer druggable gene discovery: a novel machine learning-based approach. - Wubin Ding, Diljeet Kaur, Steve Horvath, Wanding Zhou:
Comparative epigenome analysis using Infinium DNA methylation BeadChips. - Kaili Wang, Renyi Zhou, Yifan Wu, Min Li:
RLBind: a deep learning method to predict RNA-ligand binding sites. - Yifang Wei, Lingmei Li, Xin Zhao, Haitao Yang, Jian Sa, Hongyan Cao, Yuehua Cui:
Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning. - Simon Orozco-Arias, Luis Humberto López-Murillo, Mariana S. Candamil-Cortes, Maradey Arias, Paula A. Jaimes, Alexandre Rossi Paschoal, Reinel Tabares-Soto, Gustavo A. Isaza, Romain Guyot:
Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes. - Lihua Wang, Tao Zhang, Lihong Yu, Chun-Hou Zheng, Wenguang Yin, Junfeng Xia, Tiejun Zhang:
Deleterious synonymous mutation identification based on selective ensemble strategy. - Ze-Qun Zhang, Junlin Xu, Yanan Wu, Nian-Nian Liu, Ying-Long Wang, Ying Liang:
CapsNet-LDA: predicting lncRNA-disease associations using attention mechanism and capsule network based on multi-view data. - Yiming Xu, Bowen Zheng, Xiaohong Liu, Tao Wu, Jinxiu Ju, Shi-jie Wang, Yufan Lian, Hongjun Zhang, Tong Liang, Ye Sang, Rui Jiang, Guangyu Wang, Jie Ren, Ting Chen:
Improving artificial intelligence pipeline for liver malignancy diagnosis using ultrasound images and video frames. - Gabriel Cia, Fabrizio Pucci, Marianne Rooman:
Critical review of conformational B-cell epitope prediction methods. - Siqin Zhang, Kuo Yang, Zhenhong Liu, Xinxing Lai, Zhen Yang, Jianyang Zeng, Shao Li:
DrugAI: a multi-view deep learning model for predicting drug-target activating/inhibiting mechanisms. - Shu-Guang Ge, Jian Liu, Yuhu Cheng, Xiaojing Meng, Xuesong Wang:
Multi-view spectral clustering with latent representation learning for applications on multi-omics cancer subtyping. - Huiyu Li, Chen Shen, Gongji Wang, Qinru Sun, Kai Yu, Zefeng Li, Xinggong Liang, Run Chen, Hao Wu, Fan Wang, Zhenyuan Wang, Chunfeng Lian:
BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference. - Donghyo Kim, Doyeon Ha, Kwanghwan Lee, Heetak Lee, Inhae Kim, Sanguk Kim:
An evolution-based machine learning to identify cancer type-specific driver mutations. - Zhangxin Zhao, Qianjin Feng, Yu Zhang, Zhenyuan Ning:
Adaptive risk-aware sharable and individual subspace learning for cancer survival analysis with multi-modality data. - Zhiqiang Hu, Wenfeng Liu, Chenbin Zhang, Jiawen Huang, Shaoting Zhang, Huiqun Yu, Yi Xiong, Hao Liu, Song Ke, Liang Hong:
SAM-DTA: a sequence-agnostic model for drug-target binding affinity prediction. - Jianbo Fu, Qingxia Yang, Yongchao Luo, Song Zhang, Jing Tang, Ying Zhang, Hongning Zhang, Hanxiang Xu, Feng Zhu:
Label-free proteome quantification and evaluation. - Qi Liang, Wenxiang Zhang, Hao Wu, Bin Liu:
LncRNA-disease association identification using graph auto-encoder and learning to rank. - Alessia Buratin, Stefania Bortoluzzi, Enrico Gaffo:
Systematic benchmarking of statistical methods to assess differential expression of circular RNAs. - Risa Karakida Kawaguchi, Ziqi Tang, Stephan Fischer, Chandana Rajesh, Rohit Tripathy, Peter K. Koo, Jesse A. Gillis:
Learning single-cell chromatin accessibility profiles using meta-analytic marker genes. - Hailin Feng, Dongdong Jin, Jian Li, Yane Li, Quan Zou, Tongcun Liu:
Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations. - Bihan Shen, Fangyoumin Feng, Kunshi Li, Ping Lin, Liangxiao Ma, Hong Li:
A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications. - Ruyun Hu, Lihao Fu, Yongcan Chen, Junyu Chen, Yu Qiao, Tong Si:
Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments. - Mengqi Luo, Shangfu Li, Yuxuan Pang, Lantian Yao, Renfei Ma, Hsi-Yuan Huang, Hsien-Da Huang, Tzong-Yi Lee:
Extraction of microRNA-target interaction sentences from biomedical literature by deep learning approach. - Zechen Wang, Liangzhen Zheng, Sheng Wang, Mingzhi Lin, Zhihao Wang, Adams Wai-Kin Kong, Yuguang Mu, Yanjie Wei, Weifeng Li:
A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function. - Yu Zhao, Bing He, Zhimeng Xu, Yidan Zhang, Xuan Zhao, Zhi-An Huang, Fan Yang, Liang Wang, Lei Duan, Jiangning Song, Jianhua Yao:
Interpretable artificial intelligence model for accurate identification of medical conditions using immune repertoire. - Erik Christensen, Ping Luo, Andrei L. Turinsky, Mia Husic, Alaina Mahalanabis, Alaine Naidas, Juan Javier Díaz-Mejía, Michael Brudno, Trevor J. Pugh, Arun K. Ramani, Parisa Shooshtari:
Evaluation of single-cell RNAseq labelling algorithms using cancer datasets. - Xiaosa Zhao, Jun Wu, Xiaowei Zhao, Minghao Yin:
Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction. - Lei Sun, Gongming Wang, Zhihua Zhang:
SimCH: simulation of single-cell RNA sequencing data by modeling cellular heterogeneity at gene expression level. - Xinyi Xu, Xiangjie Li:
Structure-preserved dimension reduction using joint triplets sampling for multi-batch integration of single-cell transcriptomic data. - Lu Yang, Jun Chen:
Benchmarking differential abundance analysis methods for correlated microbiome sequencing data. - Chun-Jie Liu, Fei-Fei Hu, Gui-Yan Xie, Ya-Ru Miao, Xin-Wen Li, Yan Zeng, An-Yuan Guo:
GSCA: an integrated platform for gene set cancer analysis at genomic, pharmacogenomic and immunogenomic levels. - Zerun Lin, Le Ou-Yang:
Inferring gene regulatory networks from single-cell gene expression data via deep multi-view contrastive learning. - Chonghao Wang, Jing Zhang, Werner Pieter Veldsman, Xin Zhou, Lu Zhang:
A comprehensive investigation of statistical and machine learning approaches for predicting complex human diseases on genomic variants. - Yitian Fang, Fan Xu, Lesong Wei, Yi Jiang, Jie Chen, Leyi Wei, Dong-Qing Wei:
AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning. - Correction to: VPatho: a deep learning-based two-stage approach for accurate prediction of gain-of-function and loss-of-function variants.
- George Minadakis, Marios Tomazou, Nikolas Dietis, George M. Spyrou:
Vir2Drug: a drug repurposing framework based on protein similarities between pathogens. - Andrew Cheng, Guanyu Hu, Wei Vivian Li:
Benchmarking cell-type clustering methods for spatially resolved transcriptomics data. - Wujuan Zhong, Aparna Chhibber, Lan Luo, Devan V. Mehrotra, Judong Shen:
A fast and powerful linear mixed model approach for genotype-environment interaction tests in large-scale GWAS. - Yang Yue, Yongxuan Liu, Luoying Hao, Huangshu Lei, Shan He:
Improving therapeutic synergy score predictions with adverse effects using multi-task heterogeneous network learning. - Marta Nazzari, Duncan Hauser, Marcel van Herwijnen, Mírian Romitti, Daniel J. Carvalho, Anna M. Kip, Florian Caiment:
CODA: a combo-Seq data analysis workflow. - Huan Hu, Zhen Feng, Hai Lin, Junjie Zhao, Yaru Zhang, Fei Xu, Lingling Chen, Feng Chen, Yunlong Ma, Jianzhong Su, Qi Zhao, Jianwei Shuai:
Modeling and analyzing single-cell multimodal data with deep parametric inference. - Sumeet Patiyal, Anjali Dhall, Khushboo Bajaj, Harshita Sahu, Gajendra P. S. Raghava:
Prediction of RNA-interacting residues in a protein using CNN and evolutionary profile. - Yi-Zhou He, Yue Yang, Xiao-Rui Su, Bo-Wei Zhao, Shengwu Xiong, Lun Hu:
Incorporating higher order network structures to improve miRNA-disease association prediction based on functional modularity. - Zimeng Li, Shichao Zhu, Bin Shao, Xiangxiang Zeng, Tong Wang, Tie-Yan Liu:
DSN-DDI: an accurate and generalized framework for drug-drug interaction prediction by dual-view representation learning. - Lifang Yang, Ye Yang, Luqi Huang, Xiuming Cui, Yuan Liu:
From single- to multi-omics: future research trends in medicinal plants. - Joel Defo, Denis Awany, Raj Ramesar:
From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies? - Karel K. M. Koudijs, Stefan Böhringer, Henk-Jan Guchelaar:
Validation of transcriptome signature reversion for drug repurposing in oncology. - Hai-Yun Wang, Jian-Ping Zhao, Chun-Hou Zheng, Yan-Sen Su:
scGMAAE: Gaussian mixture adversarial autoencoders for diversification analysis of scRNA-seq data. - Marwan Abdellah, Juan Jose Garcia-Cantero, Nadir Román Guerrero, Alessandro Foni, Jay S. Coggan, Corrado Calì, Marco Agus, Eleftherios Zisis, Daniel Keller, Markus Hadwiger, Pierre J. Magistretti, Henry Markram, Felix Schürmann:
Ultraliser: a framework for creating multiscale, high-fidelity and geometrically realistic 3D models for in silico neuroscience. - Hong Wang, Chong Dai, Yuqi Wen, Xiaoqi Wang, Wenjuan Liu, Song He, Xiaochen Bo, Shaoliang Peng:
GADRP: graph convolutional networks and autoencoders for cancer drug response prediction. - Dong Wang, Zhenxing Wu, Chao Shen, Lingjie Bao, Hao Luo, Zhe Wang, Hucheng Yao, De-Xin Kong, Cheng Luo, Tingjun Hou:
Learning with uncertainty to accelerate the discovery of histone lysine-specific demethylase 1A (KDM1A/LSD1) inhibitors. - Tianyuan Liu, Bohao Zou, Manman He, Yongfei Hu, Yiying Dou, Tianyu Cui, Puwen Tan, Shaobin Li, Shuan Rao, Yan Huang, Sixi Liu, Kaican Cai, Dong Wang:
LncReader: identification of dual functional long noncoding RNAs using a multi-head self-attention mechanism. - Muhammad Nabeel Asim, Muhammad Ali Ibrahim, Ahtisham Fazeel, Andreas Dengel, Sheraz Ahmed:
DNA-MP: a generalized DNA modifications predictor for multiple species based on powerful sequence encoding method. - Chun-Qiu Xia, Shi-Hao Feng, Ying Xia, Xiaoyong Pan, Hong-Bin Shen:
Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network. - Yongchao Luo, Panpan Wang, Minjie Mou, Hanqi Zheng, Jiajun Hong, Lin Tao, Feng Zhu:
A novel strategy for designing the magic shotguns for distantly related target pairs. - Jingxuan Zhao, Jianqiang Sun, Stella C. Shuai, Qi Zhao, Jianwei Shuai:
Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods. - Lingling Wang, Lei Xu, Zhe Wang, Tingjun Hou, Haiping Hao, Huiyong Sun:
Cooperation of structural motifs controls drug selectivity in cyclin-dependent kinases: an advanced theoretical analysis. - Softya Sebastian, Swarup Roy, Jugal Kalita:
A generic parallel framework for inferring large-scale gene regulatory networks from expression profiles: application to Alzheimer's disease network. - Jing-Zhe Jiang, Wen-Guang Yuan, Jiayu Shang, Ying-Hui Shi, Li-Ling Yang, Min Liu, Peng Zhu, Tao Jin, Yanni Sun, Li-Hong Yuan:
Virus classification for viral genomic fragments using PhaGCN2. - Zhi-Jian Zhou, Chen-Hui Yang, Sheng-Bao Ye, Xiao-Wei Yu, Ye Qiu, Xing-Yi Ge:
VirusRecom: an information-theory-based method for recombination detection of viral lineages and its application on SARS-CoV-2. - Yanqiang Han, Zhilong Wang, An Chen, Imran Ali, Junfei Cai, Simin Ye, Zhiyun Wei, Jin-Jin Li:
A deep transfer learning-based protocol accelerates full quantum mechanics calculation of protein. - Cheng-Hong Yang, Ming-Feng Hou, Li-Yeh Chuang, Cheng-San Yang, Yu-Da Lin:
Dimensionality reduction approach for many-objective epistasis analysis. - Han Yu, Xiaozhou Luo:
IPPF-FE: an integrated peptide and protein function prediction framework based on fused features and ensemble models. - Yang Yang, Yuwei Lu, Wenying Yan:
A comprehensive review on knowledge graphs for complex diseases. - Nisha Bajiya, Anjali Dhall, Suchet Aggarwal, Gajendra P. S. Raghava:
Advances in the field of phage-based therapy with special emphasis on computational resources. - Zhenshuang Tang, Lilin Yin, Dong Yin, Haohao Zhang, Yuhua Fu, Guangliang Zhou, Yunxiang Zhao, Zhiquan Wang, Xiaolei Liu, Xinyun Li, Shuhong Zhao:
Development and application of an efficient genomic mating method to maximize the production performances of three-way crossbred pigs. - Siqi Chen, Xuhua Yan, Ruiqing Zheng, Min Li:
Bubble: a fast single-cell RNA-seq imputation using an autoencoder constrained by bulk RNA-seq data. - Qilong Wu, Sheng-You Huang:
HCovDock: an efficient docking method for modeling covalent protein-ligand interactions. - Zixi Zheng, Yanyan Tan, Hong Wang, Shengpeng Yu, Tianyu Liu, Cheng Liang:
CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction. - Jing Liu, Xinghua Tang, Xiao Guan:
Grain protein function prediction based on self-attention mechanism and bidirectional LSTM.