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ORCIDBriefings in Bioinformatics, Volume 23
Volume 23, Number 1, January 2022
- Umm-Kulthum Ismail Umlai
, Dhinoth Kumar Bangarusamy, Xavier Estivill, Puthen Veettil Jithesh:
Genome sequencing data analysis for rare disease gene discovery. - Wending Tang, Ruyu Dai, Wenhui Yan, Wei Zhang, Yannan Bin, En-Hua Xia, Junfeng Xia:
Identifying multi-functional bioactive peptide functions using multi-label deep learning. - Robson Bonidia, Douglas Silva Domingues, Danilo Sipoli Sanches, André C. P. L. F. de Carvalho:
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors. - Haochen Zhao, Shaokai Wang, Kai Zheng, Qichang Zhao, Feng Zhu, Jianxin Wang:
A similarity-based deep learning approach for determining the frequencies of drug side effects. - Peiran Jiang, Ying Chi, Xiao-Shuang Li, Xiang Liu, Xian-Sheng Hua, Kelin Xia:
Molecular persistent spectral image (Mol-PSI) representation for machine learning models in drug design. - Yaqi Wang, Guoqin Mai, Min Zou, Haoyu Long, Yao-Qing Chen, Litao Sun, Dechao Tian, Yang Zhao, Guozhi Jiang, Zicheng Cao, Xiangjun Du:
Heavy chain sequence-based classifier for the specificity of human antibodies. - Qingyong Wang, Yun Zhou:
FedSPL: federated self-paced learning for privacy-preserving disease diagnosis. - Eun-Gyeong Park, Sung-Jin Pyo, Youxi Cui, Sang-Ho Yoon, Jin-Wu Nam:
Tumor immune microenvironment lncRNAs. - Hui Li, Zhaohong Deng, Haitao Yang, Xiaoyong Pan, Zhisheng Wei, Hong-Bin Shen, Kup-Sze Choi, Lei Wang, Shitong Wang, Jing Wu:
circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier. - Kyle Hippe, Cade Lilley, Joshua William Berkenpas, Ciri Chandana Pocha, Kiyomi Kishaba, Hui Ding, Jie Hou, Dong Si, Renzhi Cao:
ZoomQA: residue-level protein model accuracy estimation with machine learning on sequential and 3D structural features. - Ran Su, Yingying Zhu, Quan Zou, Leyi Wei:
Distant metastasis identification based on optimized graph representation of gene interaction patterns. - Angela Serra, Michele Fratello, Antonio Federico, Ravi Ojha, Riccardo Provenzani, Ervin Tasnádi, Luca Cattelani, Giusy del Giudice, Pia Anneli Sofia Kinaret, Laura Aliisa Saarimäki, Alisa Pavel, Suvi Kuivanen, Vincenzo Cerullo, Olli Vapalahti, Peter Horváth, Antonio Di Lieto, Jari Yli-Kauhaluoma, Giuseppe Balistreri, Dario Greco:
Computationally prioritized drugs inhibit SARS-CoV-2 infection and syncytia formation. - Jinxian Wang, Ying Zhang, Wenjuan Nie, Yi Luo, Lei Deng:
Computational anti-COVID-19 drug design: progress and challenges. - Jhabindra Khanal, Hilal Tayara, Quan Zou, Kil To Chong:
DeepCap-Kcr: accurate identification and investigation of protein lysine crotonylation sites based on capsule network. - Yingxi Yang, Quan Sun, Le Huang, Jai G. Broome, Adolfo Correa, Alexander Reiner, Laura M. Raffield, Yuchen Yang, Yun Li:
eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data. - Fei Wang, Xiujuan Lei, Bo Liao, Fang-Xiang Wu:
Predicting drug-drug interactions by graph convolutional network with multi-kernel. - Yongqing Zhang, Zixuan Wang, Yuanqi Zeng, Yuhang Liu, Shuwen Xiong, Maocheng Wang, Jiliu Zhou, Quan Zou:
A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape. - Qiguo Dai, Zhaowei Wang, Ziqiang Liu, Xiaodong Duan, Jinmiao Song, Maozu Guo:
Predicting miRNA-disease associations using an ensemble learning framework with resampling method. - Lianlian Wu, Yuqi Wen, Dongjin Leng, Qinglong Zhang, Chong Dai, Zhongming Wang, Ziqi Liu, Bowei Yan, Yixin Zhang, Jing Wang, Song He, Xiaochen Bo:
Machine learning methods, databases and tools for drug combination prediction. - Shuangquan Zhang, Anjun Ma, Jing Zhao, Dong Xu, Qin Ma, Yan Wang:
Assessing deep learning methods in cis-regulatory motif finding based on genomic sequencing data. - Wei Wang, Ruijiang Han, Menghan Zhang, Yuxian Wang, Tao Wang, Yongtian Wang, Xuequn Shang, Jiajie Peng:
A network-based method for brain disease gene prediction by integrating brain connectome and molecular network. - He Li, Hangxiao Zhang, Hangjin Jiang:
Combining power of different methods to detect associations in large data sets. - Ling Gao, Hui Cui, Tiangang Zhang, Nan Sheng, Ping Xuan:
Prediction of drug-disease associations by integrating common topologies of heterogeneous networks and specific topologies of subnets. - Guangzhan Zhang, Menglu Li, Huan Deng, Xinran Xu, Xuan Liu, Wen Zhang:
SGNNMD: signed graph neural network for predicting deregulation types of miRNA-disease associations. - Zhao Chen, Yin Jiang, Xiaoyu Zhang, Rui Zheng, Ruijin Qiu, Yang Sun, Chen Zhao, Hongcai Shang:
ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18. - Lei Huang, Jiecong Lin, Xiangtao Li, Linqi Song, Zetian Zheng, Ka-Chun Wong:
EGFI: drug-drug interaction extraction and generation with fusion of enriched entity and sentence information. - Farzaneh Firoozbakht, Behnam Yousefi, Benno Schwikowski:
An overview of machine learning methods for monotherapy drug response prediction. - Xin An, Xi Chen, Daiyao Yi, Hongyang Li, Yuanfang Guan:
Representation of molecules for drug response prediction. - Ke Han, Long-Chen Shen, Yi-Heng Zhu, Jian Xu, Jiangning Song, Dong-Jun Yu:
MAResNet: predicting transcription factor binding sites by combining multi-scale bottom-up and top-down attention and residual network. - Xin Li, Xu Pan, Hanxiao Zhou, Peng Wang, Yue Gao, Shipeng Shang, Shuang Guo, Jie Sun, Zhiying Xiong, Shangwei Ning, Hui Zhi, Xia Li:
Comprehensive characterization genetic regulation and chromatin landscape of enhancer-associated long non-coding RNAs and their implication in human cancer. - Jianyuan Deng, Zhibo Yang, Iwao Ojima, Dimitris Samaras, Fusheng Wang:
Artificial intelligence in drug discovery: applications and techniques. - Xinyu Yu, Likun Jiang, Shuting Jin, Xiangxiang Zeng, Xiangrong Liu:
preMLI: a pre-trained method to uncover microRNA-lncRNA potential interactions. - Yingxin Kan, Limin Jiang, Yan Guo, Jijun Tang, Fei Guo:
Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes. - Adel Mehrpooya, Farid Saberi Movahed, Najmeh Azizi Zadeh, Mohammad Rezaei-Ravari, Farshad Saberi-Movahed, Mahdi Eftekhari, Iman Tavassoly:
High dimensionality reduction by matrix factorization for systems pharmacology. - Yi Yang, Xingjie Shi, Wei Liu, Qiuzhong Zhou, Mai Chan Lau, Jeffrey Chun Tatt Lim, Lei Sun, Cedric Chuan Young Ng, Joe Yeong, Jin Liu:
SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes. - Robert Schwarz, Philipp Koch, Jeanne Wilbrandt, Steve Hoffmann:
Locus-specific expression analysis of transposable elements. - Qian Cheng, Shuqing Jiang, Feng Xu, Qian Wang, Yingjie Xiao, Ruyang Zhang, Jiuran Zhao, Jianbing Yan, Chuang Ma, Xiangfeng Wang:
Genome optimization via virtual simulation to accelerate maize hybrid breeding. - Nicoleta Siminea, Victor-Bogdan Popescu, José Ángel Sánchez Martín, Daniela Florea, Georgiana Gavril, Ana Maria Gheorghe, Corina Itcus, Krishna Kanhaiya, Octavian Pacioglu, Laura Ioana Popa, Romica Trandafir, Maria Iris Tusa, Manuela Sidoroff, Mihaela Paun, Eugen Czeizler, Andrei Paun, Ion Petre:
Network analytics for drug repurposing in COVID-19. - Leyun Wu, Cheng Peng, Yanqing Yang, Yulong Shi, Liping Zhou, Zhijian Xu, Weiliang Zhu:
Exploring the immune evasion of SARS-CoV-2 variant harboring E484K by molecular dynamics simulations. - Yang Guo, Fatemeh Esfahani, Xiaojian Shao, Venkatesh Srinivasan, Alex Thomo, Li Xing, Xuekui Zhang:
Integrative COVID-19 biological network inference with probabilistic core decomposition. - Chuanxing Li, Jing Gao, Zicheng Zhang, Lu Chen, Xun Li, Meng Zhou, Åsa M. Wheelock:
Multiomics integration-based molecular characterizations of COVID-19. - Hongfei Li, Yue Gong, Yifeng Liu, Hao Lin, Guohua Wang:
Detection of transcription factors binding to methylated DNA by deep recurrent neural network. - Lirong Zhang, Yanchao Yang, Lu Chai, Qianzhong Li, Junjie Liu, Hao Lin, Li Liu:
A deep learning model to identify gene expression level using cobinding transcription factor signals. - Fuyi Li, Shuangyu Dong, André Leier, Meiya Han, Xudong Guo, Jing Xu, Xiaoyu Wang, Shirui Pan, Cangzhi Jia, Yang Zhang, Geoffrey I. Webb, Lachlan J. M. Coin, Chen Li, Jiangning Song:
Positive-unlabeled learning in bioinformatics and computational biology: a brief review. - Shenggeng Lin, Yanjing Wang, Lingfeng Zhang, Yanyi Chu, Yatong Liu, Yitian Fang, Mingming Jiang, Qiankun Wang, Bowen Zhao, Yi Xiong, Dong-Qing Wei:
MDF-SA-DDI: predicting drug-drug interaction events based on multi-source drug fusion, multi-source feature fusion and transformer self-attention mechanism. - Yeji Wang, Shuo Wu, Yanwen Duan, Yong Huang:
A point cloud-based deep learning strategy for protein-ligand binding affinity prediction. - Jialu Hu, Yuanke Zhong, Xuequn Shang:
A versatile and scalable single-cell data integration algorithm based on domain-adversarial and variational approximation. - Menglu Li, Wen Zhang:
PHIAF: prediction of phage-host interactions with GAN-based data augmentation and sequence-based feature fusion. - Wei Zhang, Hanwen Xu, Rong Qiao, Bixi Zhong, Xianglin Zhang, Jin Gu, Xuegong Zhang, Lei Wei, Xiaowo Wang:
ARIC: accurate and robust inference of cell type proportions from bulk gene expression or DNA methylation data. - ZiaurRehman Tanoli, Jehad Aldahdooh, Farhan Alam, Yinyin Wang, Umair Seemab, Maddalena Fratelli, Petr Pavlis, Marián Hajdúch, Florence Bietrix, Philip Gribbon, Andrea Zaliani, Matthew D. Hall, Min Shen, Kyle R. Brimacombe, Evgeny Kulesskiy, Saarela Jani, Krister Wennerberg, Markus Vähä-Koskela, Jing Tang:
Minimal information for chemosensitivity assays (MICHA): a next-generation pipeline to enable the FAIRification of drug screening experiments. - Bingxiang Xu, Xiaoli Li, Xiaomeng Gao, Yan Jia, Jing Liu, Feifei Li, Zhihua Zhang:
DeNOPA: decoding nucleosome positions sensitively with sparse ATAC-seq data. - Lihua Jia, Wen Yao, Yingru Jiang, Yang Li, Zhizhan Wang, Haoran Li, Fangfang Huang, Jiaming Li, Tiantian Chen, Huiyong Zhang:
Development of interactive biological web applications with R/Shiny. - Hukam Chand Rawal, Shakir Ali, Tapan Kumar Mondal:
miRPreM and tiRPreM: Improved methodologies for the prediction of miRNAs and tRNA-induced small non-coding RNAs for model and non-model organisms. - Thanh-Binh Nguyen, Douglas E. V. Pires, David B. Ascher:
CSM-carbohydrate: protein-carbohydrate binding affinity prediction and docking scoring function. - Diego Forni, Rachele Cagliani, Chiara Pontremoli, Mario Clerici, Manuela Sironi:
The substitution spectra of coronavirus genomes. - Haoxiang Qin, Qidong Shen, Hongyi Zhao, Guozhen Qi, Lei Gao:
Network-based analysis revealed significant interactions between risk genes of severe COVID-19 and host genes interacted with SARS-CoV-2 proteins. - Hong Wang, Jingqing Zhang, Zhigang Lu, Weina Dai, Chuanjiang Ma, Yun Xiang, Yonghong Zhang:
Identification of potential therapeutic targets and mechanisms of COVID-19 through network analysis and screening of chemicals and herbal ingredients. - Ngoc Hieu Tran, Jinbo Xu, Ming Li:
A tale of solving two computational challenges in protein science: neoantigen prediction and protein structure prediction. - Wei Lan, Yi Dong, Qingfeng Chen, Ruiqing Zheng, Jin Liu, Yi Pan, Yi-Ping Phoebe Chen:
KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network. - Sander N. Goossens, Tim H. Heupink, Elise De Vos, Anzaan Dippenaar, Margaretha De Vos, Rob Warren, Annelies Van Rie:
Detection of minor variants in Mycobacterium tuberculosis whole genome sequencing data. - Margaret G. Guo, Daniel N. Sosa, Russ B. Altman:
Challenges and opportunities in network-based solutions for biological questions. - Xiao-Rui Su, Lun Hu, Zhuhong You, Pengwei Hu, Lei Wang, Bo-Wei Zhao:
A deep learning method for repurposing antiviral drugs against new viruses via multi-view nonnegative matrix factorization and its application to SARS-CoV-2. - Neng Huang, Fan Nie, Peng Ni, Xin Gao, Feng Luo, Jianxin Wang:
BlockPolish: accurate polishing of long-read assembly via block divide-and-conquer. - Xu Pan, Caiyu Zhang, Junwei Wang, Peng Wang, Yue Gao, Shipeng Shang, Shuang Guo, Xin Li, Hui Zhi, Shangwei Ning:
Epigenome signature as an immunophenotype indicator prompts durable clinical immunotherapy benefits in lung adenocarcinoma. - Rufeng Li, Lixin Li, Yungang Xu, Juan Yang:
Erratum to: Machine learning meets omics applications and perspectives. - Deepak Nag Ayyala, Jianan Lin, Zhengqing Ouyang:
Differential RNA methylation using multivariate statistical methods. - Xinyun Guo, Huan He, Jialin Yu, Shaoping Shi:
PKSPS: a novel method for predicting kinase of specific phosphorylation sites based on maximum weighted bipartite matching algorithm and phosphorylation sequence enrichment analysis. - Cui-Xiang Lin, Hong-Dong Li, Chao Deng, Weisheng Liu, Shannon Erhardt, Fang-Xiang Wu, Xing-Ming Zhao, Yuanfang Guan, Jun Wang, Daifeng Wang, Bin Hu, Jianxin Wang:
An integrated brain-specific network identifies genes associated with neuropathologic and clinical traits of Alzheimer's disease. - Yuxin Song, Li'ang Yang, Li Jiang, Zhiyu Hao, Runqing Yang, Pao Xu:
Optimizing genomic control in mixed model associations with binary diseases. - Héctor Buena Maizón, Francisco J. Barrantes:
A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor. - Daudi Jjingo, Gerald Mboowa, Ivan Sserwadda, Robert Kakaire, Davis Kiberu, Marion Amujal, Ronald Galiwango, David Kateete, Moses Joloba, Christopher C. Whalen:
Bioinformatics mentorship in a resource limited setting. - Huan Liu, Quan Zou, Yun Xu:
A novel fast multiple nucleotide sequence alignment method based on FM-index. - Véronique Duboc, David Pratella, Marco Milanesio, John Boudjarane, Stéphane Descombes, Véronique Paquis-Flucklinger, Silvia Bottini:
NiPTUNE: an automated pipeline for noninvasive prenatal testing in an accurate, integrative and flexible framework. - Mingon Kang, Euiseong Ko, Tesfaye B. Mersha:
A roadmap for multi-omics data integration using deep learning. - Le Ou-Yang, Fan Lu, Zi-Chao Zhang, Min Wu:
Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. - Fangfang Xia, Jonathan E. Allen, Prasanna Balaprakash, Thomas S. Brettin, Cristina Garcia-Cardona, Austin Clyde, Judith D. Cohn, James H. Doroshow, Xiaotian Duan, Veronika Dubinkina, Yvonne A. Evrard, Ya Ju Fan, Jason Gans, Stewart He, Pinyi Lu, Sergei Maslov, Alexander Partin, Maulik Shukla, Eric A. Stahlberg, Justin M. Wozniak, Hyun Seung Yoo, George F. Zaki, Yitan Zhu, Rick Stevens:
A cross-study analysis of drug response prediction in cancer cell lines. - Song Zhang, Kuerbannisha Amahong, Chenyang Zhang, Fengcheng Li, Jianqing Gao, Yunqing Qiu, Feng Zhu:
RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection. - Liang Yu, Mingfei Xia, Qi An:
A network embedding framework based on integrating multiplex network for drug combination prediction. - Jinxian Wang, Xuejun Liu, Siyuan Shen, Lei Deng, Hui Liu:
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations. - Bo-Wei Zhao, Lun Hu, Zhu-Hong You, Lei Wang, Xiao-Rui Su:
HINGRL: predicting drug-disease associations with graph representation learning on heterogeneous information networks. - Ye Hong, Dani Flinkman, Tomi Suomi, Sami Pietilä, Peter James, Eleanor Coffey, Laura L. Elo:
PhosPiR: an automated phosphoproteomic pipeline in R. - Meng-Huan Song, Chaochao Yan, Jiatang Li:
MEANGS: an efficient seed-free tool for de novo assembling animal mitochondrial genome using whole genome NGS data. - Qingyang Yin, Yang Wang, Jinting Guan, Guoli Ji:
scIAE: an integrative autoencoder-based ensemble classification framework for single-cell RNA-seq data. - Hao Wu, Yingfu Wu, Yuhong Jiang, Bing Zhou, Haoru Zhou, Zhongli Chen, Yi Xiong, Quanzhong Liu, Hongming Zhang:
scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding. - Zhen Cao, Yanting Huang, Ran Duan, Peng Jin, Zhaohui S. Qin, Shihua Zhang:
Disease category-specific annotation of variants using an ensemble learning framework. - Jeremiah Suryatenggara, Kol Jia Yong, Danielle E. Tenen, Daniel G. Tenen, Mahmoud A. Bassal:
ChIP-AP: an integrated analysis pipeline for unbiased ChIP-seq analysis. - Bo Zhang, Jianghua He, Jinxiang Hu, Devin C. Koestler, Prabhakar Chalise:
Letter to the Editor: on the stability and internal consistency of component-wise sparse mixture regression-based clustering. - Jiecong Lin, Lei Huang, Xingjian Chen, Shixiong Zhang, Ka-Chun Wong:
DeepMotifSyn: a deep learning approach to synthesize heterodimeric DNA motifs. - Fabrizio Kuruc, Harald Binder, Moritz Hess:
Stratified neural networks in a time-to-event setting. - Olufemi Aromolaran, Damilare Aromolaran, Itunuoluwa Isewon, Jelili Oyelade:
Corrigendum to: Machine learning approach to gene essentiality prediction: a review. - Xiwen Zhang, Weiwen Wang, Chuan-Xian Ren, Dao-Qing Dai:
Learning representation for multiple biological networks via a robust graph regularized integration approach. - Xu Zhang, Zhiqiang Ye, Jing Chen, Feng Qiao:
AMDBNorm: an approach based on distribution adjustment to eliminate batch effects of gene expression data. - Ashwin Dhakal, Cole McKay, John J. Tanner, Jianlin Cheng:
Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions. - Chun-Chun Wang, Chi-Chi Zhu, Xing Chen:
Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease. - Dan Shao, Yinfei Dai, Nianfeng Li, Xuqing Cao, Wei Zhao, Li Cheng, Zhuqing Rong, Lan Huang, Yan Wang, Jing Zhao:
Artificial intelligence in clinical research of cancers. - Weining Yuan, Guanxing Chen, Calvin Yu-Chian Chen:
FusionDTA: attention-based feature polymerizer and knowledge distillation for drug-target binding affinity prediction. - María Virginia Sabando, Ignacio Ponzoni, Evangelos E. Milios, Axel J. Soto:
Using molecular embeddings in QSAR modeling: does it make a difference? - Francesco Napolitano, Xiaopeng Xu, Xin Gao:
Impact of computational approaches in the fight against COVID-19: an AI guided review of 17 000 studies. - Lis Arend, Judith Bernett, Quirin Manz, Melissa Klug, Olga Lazareva, Jan Baumbach, Dario Bongiovanni, Markus List:
A systematic comparison of novel and existing differential analysis methods for CyTOF data. - Priyank Shukla, Preeti Pandey, Bodhayan Prasad, Tony Robinson, Rituraj Purohit, Leon G. D'cruz, Murtaza M. Tambuwala, Ankur Mutreja, Jim Harkin, Taranjit Singh Rai, Elaine K. Murray, David S. Gibson, Anthony J. Bjourson:
Immuno-informatics analysis predicts B and T cell consensus epitopes for designing peptide vaccine against SARS-CoV-2 with 99.82% global population coverage. - Arnold K. Nyamabo, Hui Yu, Zun Liu, Jian-Yu Shi:
Drug-drug interaction prediction with learnable size-adaptive molecular substructures. - Sandra L. Taylor, Matthew Ponzini, Machelle D. Wilson, Kyoungmi Kim:
Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data. - Maryam Mahjoubin-Tehran, Samaneh Rezaei, Amin Jalili, Amirhossein Sahebkar, Seyed Hamid Aghaee-Bakhtiari:
A comprehensive review of online resources for microRNA-diseases associations: the state of the art. - Yurui Chen, Louxin Zhang:
How much can deep learning improve prediction of the responses to drugs in cancer cell lines? - Yu-Jian Kang, Jing-Yi Li, Lan Ke, Shuai Jiang, Dechang Yang, Mei Hou, Ge Gao:
Quantitative model suggests both intrinsic and contextual features contribute to the transcript coding ability determination in cells. - Wenjia He, Yi Jiang, Junru Jin, Zhongshen Li, Jiaojiao Zhao, Balachandran Manavalan, Ran Su, Xin Gao, Leyi Wei:
Accelerating bioactive peptide discovery via mutual information-based meta-learning. - Ruohan Wang, Xiang-Li-Lan Zhang, Jianping Wang, Shuai Cheng Li:
DeepHost: phage host prediction with convolutional neural network. - Xiaosa Zhao, Xiaowei Zhao, Minghao Yin:
Heterogeneous graph attention network based on meta-paths for lncRNA-disease association prediction. - Mario Flores, Zhentao Liu, Tinghe Zhang, Md Musaddaqui Hasib, Yu-Chiao Chiu, Zhenqing Ye, Karla Paniagua, Sumin Jo, Jianqiu Zhang, Shou-Jiang Gao, Yu-Fang Jin, Yidong Chen, Yufei Huang:
Deep learning tackles single-cell analysis - a survey of deep learning for scRNA-seq analysis.