A review of deep learning applications in human genomics using next-generation sequencing data
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[1] Carlos Loucera,et al. Integrating pathway knowledge with deep neural networks to reduce the dimensionality in single-cell RNA-seq data , 2021, BioData Mining.
[2] Xiao-Meng Zhang,et al. Graph Neural Networks and Their Current Applications in Bioinformatics , 2021, Frontiers in Genetics.
[3] Iqbal H. Sarker. Machine Learning: Algorithms, Real-World Applications and Research Directions , 2021, SN Computer Science.
[4] Karsten M. Borgwardt,et al. Biological network analysis with deep learning , 2020, Briefings Bioinform..
[5] Clayton Barham,et al. Interpretation of deep learning in genomics and epigenomics , 2020, Briefings Bioinform..
[6] Chenguang Zhu,et al. Deep learning in natural language processing , 2021, Machine Reading Comprehension.
[7] C. Wang,et al. Machine learning in additive manufacturing: State-of-the-art and perspectives , 2020, Additive Manufacturing.
[8] Jiangning Song,et al. DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases. , 2020, Briefings in bioinformatics.
[9] Jianzhu Ma,et al. Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells. , 2020, Cancer cell.
[10] B. Guldbrandtsen,et al. The application of deep learning for the classification of correct and incorrect SNP genotypes from whole-genome DNA sequencing pipelines , 2020, Journal of Applied Genetics.
[11] A. Akalin,et al. Deep learning for genomics using Janggu , 2020, Nature Communications.
[12] Lefteris Koumakis,et al. Deep learning models in genomics; are we there yet? , 2020, Computational and structural biotechnology journal.
[13] Marta R. Hidalgo,et al. Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscape , 2020, NAR cancer.
[14] J. Shendure,et al. Predicting mRNA Abundance Directly from Genomic Sequence Using Deep Convolutional Neural Networks. , 2020, Cell reports.
[15] Maha A. Thafar,et al. Splice2Deep: An ensemble of deep convolutional neural networks for improved splice site prediction in genomic DNA , 2020, Gene: X.
[16] Mingyao Li,et al. Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis , 2020, Nature Communications.
[17] D. Chicco,et al. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation , 2020, BMC Genomics.
[18] V. Verendel,et al. Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure , 2019, Nature Communications.
[19] Ekta Khurana,et al. A deep learning approach to predict the impact of non-coding sequence variants on 3D chromatin structure , 2019, bioRxiv.
[20] Tim Kacprowski,et al. DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning , 2020, PLoS Comput. Biol..
[21] Miguel Rocha,et al. Deep learning for drug response prediction in cancer , 2020, Briefings Bioinform..
[22] Aditya Singh,et al. Intelli-NGS: Intelligent NGS, a deep neural network-based artificial intelligence to delineate good and bad variant calls from IonTorrent sequencer data , 2019, bioRxiv.
[23] Aristotelis Tsirigos,et al. A Deep Learning Framework for Predicting Response to Therapy in Cancer. , 2019, Cell reports.
[24] Lizhen Shi,et al. Computational Strategies for Scalable Genomics Analysis , 2019, Genes.
[25] Seokho Kang,et al. Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation , 2019, Journal of Cheminformatics.
[26] Yaoqi Zhou,et al. RNA secondary structure prediction using an ensemble of two-dimensional deep neural networks and transfer learning , 2019, Nature Communications.
[27] Carlos Torroja,et al. Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data , 2019, Front. Genet..
[28] G. Gupta,et al. DAVI: Deep learning-based tool for alignment and single nucleotide variant identification , 2019, bioRxiv.
[29] E. Schadt,et al. Functional interpretation of genetic variants using deep learning predicts impact on chromatin accessibility and histone modification , 2019, Nucleic acids research.
[30] Alexander G. B. Grønning,et al. DeepCLIP: predicting the effect of mutations on protein–RNA binding with deep learning , 2019, bioRxiv.
[31] Miguel Pérez-Enciso,et al. A Guide on Deep Learning for Complex Trait Genomic Prediction , 2019, Genes.
[32] Fabian J Theis,et al. Deep learning: new computational modelling techniques for genomics , 2019, Nature Reviews Genetics.
[33] Yong Yu,et al. A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures , 2019, Neural Computation.
[34] Georg Seelig,et al. A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation , 2019, Cell.
[35] K. Kohut,et al. The Changing Role of the Genetic Counsellor in the Genomics Era , 2019, Current Genetic Medicine Reports.
[36] Jun Cheng,et al. The Kipoi repository accelerates community exchange and reuse of predictive models for genomics , 2019, Nature Biotechnology.
[37] Vladislav Lysenkov,et al. Introducing deep learning -based methods into the variant calling analysis pipeline , 2019 .
[38] Asif Ahmed Neloy,et al. Machine Learning based Health Prediction System using IBM Cloud as PaaS , 2019, 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI).
[39] Evan M. Cofer,et al. Selene: a PyTorch-based deep learning library for sequence data , 2019, Nature Methods.
[40] Yingnian Wu,et al. Deep-learning augmented RNA-seq analysis of transcript splicing , 2019, Nature Methods.
[41] W. Wong,et al. DeepTACT: predicting 3D chromatin contacts via bootstrapping deep learning , 2019, Nucleic acids research.
[42] Heather L. Mulder,et al. Analysis of error profiles in deep next-generation sequencing data , 2019, Genome Biology.
[43] Yann LeCun,et al. 1.1 Deep Learning Hardware: Past, Present, and Future , 2019, 2019 IEEE International Solid- State Circuits Conference - (ISSCC).
[44] M. Schatz,et al. A multi-task convolutional deep neural network for variant calling in single molecule sequencing , 2019, Nature Communications.
[45] Phillip M Cheng,et al. Artificial Intelligence for Medical Image Analysis: A Guide for Authors and Reviewers. , 2019, AJR. American journal of roentgenology.
[46] Yufeng Wu,et al. DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network , 2019, BMC Bioinformatics.
[47] Wenyu Wang,et al. Making Sense of the Epigenome Using Data Integration Approaches , 2019, Front. Pharmacol..
[48] Hsiao-Chun Wu,et al. DeepDrug3D: Classification of ligand-binding pockets in proteins with a convolutional neural network , 2019, PLoS Comput. Biol..
[49] David G. Knowles,et al. Predicting Splicing from Primary Sequence with Deep Learning , 2019, Cell.
[50] Peter M. Krawitz,et al. Identifying facial phenotypes of genetic disorders using deep learning , 2019, Nature Medicine.
[51] R. Jiang,et al. DeepHistone: a deep learning approach to predicting histone modifications , 2018, BMC Genom..
[52] Vladimir B. Bajic,et al. DeepGSR: an optimized deep-learning structure for the recognition of genomic signals and regions , 2018, Bioinform..
[53] Michael C. Schatz,et al. Clairvoyante: a multi-task convolutional deep neural network for variant calling in Single Molecule Sequencing , 2018, bioRxiv.
[54] M. Kunitski,et al. Double-slit photoelectron interference in strong-field ionization of the neon dimer , 2018, Nature Communications.
[55] Hamed Asadi,et al. Peering Into the Black Box of Artificial Intelligence: Evaluation Metrics of Machine Learning Methods. , 2019, AJR. American journal of roentgenology.
[56] Ashvin Bashyam,et al. Deep learning for genomics , 2018, Nature Genetics.
[57] Borut Peterlin,et al. PEDIA: prioritization of exome data by image analysis , 2018, Genetics in Medicine.
[58] M. Huss,et al. A primer on deep learning in genomics , 2018, Nature Genetics.
[59] De-Shuang Huang,et al. Recurrent Neural Network for Predicting Transcription Factor Binding Sites , 2018, Scientific Reports.
[60] Gui-Bin Bian,et al. Performance Analysis of Google Colaboratory as a Tool for Accelerating Deep Learning Applications , 2018, IEEE Access.
[61] Thomas Colthurst,et al. A universal SNP and small-indel variant caller using deep neural networks , 2018, Nature Biotechnology.
[62] Song He,et al. Deep learning-based transcriptome data classification for drug-target interaction prediction , 2018, BMC Genomics.
[63] Avanti Shrikumar,et al. Deciphering regulatory DNA sequences and noncoding genetic variants using neural network models of massively parallel reporter assays , 2018, bioRxiv.
[64] Luis Tobalina,et al. How to find the right drug for each patient? Advances and challenges in pharmacogenomics , 2018, Current opinion in systems biology.
[65] Jörg Hakenberg,et al. Predicting the clinical impact of human mutation with deep neural networks , 2018, Nature Genetics.
[66] Günter Mayer,et al. Systematic evaluation of error rates and causes in short samples in next-generation sequencing , 2018, Scientific Reports.
[67] Bharanidharan Devarajan,et al. Performance assessment of variant calling pipelines using human whole exome sequencing and simulated data , 2018, BMC Bioinformatics.
[68] Wesley De Neve,et al. SpliceRover: interpretable convolutional neural networks for improved splice site prediction , 2018, Bioinform..
[69] Yong Wang,et al. Integrating distal and proximal information to predict gene expression via a densely connected convolutional neural network , 2018, bioRxiv.
[70] Yufei Huang,et al. Predicting drug response of tumors from integrated genomic profiles by deep neural networks , 2018, BMC Medical Genomics.
[71] Chandra L. Theesfeld,et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk , 2018, Nature Genetics.
[72] R. Jiang,et al. Prediction of enhancer-promoter interactions via natural language processing , 2018, BMC Genomics.
[73] Maxat Kulmanov,et al. DeepPVP: phenotype-based prioritization of causative variants using deep learning , 2018, BMC Bioinformatics.
[74] M. DePristo,et al. Deep learning of genomic variation and regulatory network data. , 2018, Human molecular genetics.
[75] Brendan J. Frey,et al. COSSMO: predicting competitive alternative splice site selection using deep learning , 2018, bioRxiv.
[76] Zhongming Zhao,et al. Gene2vec: distributed representation of genes based on co-expression , 2018, bioRxiv.
[77] A. Badnjević,et al. Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics , 2018, BMC Medical Genomics.
[78] Annalisa Marsico,et al. pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks , 2018, Bioinform..
[79] Haohan Wang,et al. Deep Learning for Genomics: A Concise Overview , 2018, ArXiv.
[80] Feng Liu,et al. Deep Learning and Its Applications in Biomedicine , 2018, Genom. Proteom. Bioinform..
[81] S. Mohamad R. Soroushmehr,et al. Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification , 2018, Pharmacogenomics.
[82] Andreas Bender,et al. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning , 2017, Bioinform..
[83] Qiao Liu,et al. Chromatin accessibility prediction via a hybrid deep convolutional neural network , 2017, Bioinform..
[84] Cory Y. McLean,et al. Sequential regulatory activity prediction across chromosomes with convolutional neural networks , 2017, bioRxiv.
[85] Guohua Huang,et al. The Advances and Challenges of Deep Learning Application in Biological Big Data Processing , 2017, Current Bioinformatics.
[86] Pooja Asopa,et al. Conceptual Understanding of Convolutional Neural Network- A Deep Learning Approach , 2018 .
[87] Alexis B. Carter,et al. Standards and Guidelines for Validating Next-Generation Sequencing Bioinformatics Pipelines: A Joint Recommendation of the Association for Molecular Pathology and the College of American Pathologists. , 2018, The Journal of molecular diagnostics : JMD.
[88] Geoffrey E. Hinton,et al. Deep Learning for Natural Language Processing , 2021, International Journal for Research in Applied Science and Engineering Technology.
[89] Ole Winther,et al. DeepLoc: prediction of protein subcellular localization using deep learning , 2017, Bioinform..
[90] Gill Bejerano,et al. A sequence-based, deep learning model accurately predicts RNA splicing branchpoints , 2017, bioRxiv.
[91] Zenghui Wang,et al. Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review , 2017, Neural Computation.
[92] K. Eilbeck,et al. Settling the score: variant prioritization and Mendelian disease , 2017, Nature Reviews Genetics.
[93] Fabian J Theis,et al. Single cells make big data: New challenges and opportunities in transcriptomics , 2017 .
[94] Chao Ren,et al. BiRen: predicting enhancers with a deep‐learning‐based model using the DNA sequence alone , 2017, Bioinform..
[95] Daniel Quang,et al. FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data , 2017, bioRxiv.
[96] Viola Ravasio,et al. GARFIELD-NGS: Genomic vARiants FIltering by dEep Learning moDels in NGS , 2017, bioRxiv.
[97] Xiaodong Zhang,et al. Concod: an effective integration framework of consensus-based calling deletions from next-generation sequencing data , 2017, Int. J. Data Min. Bioinform..
[98] Heiko Müller,et al. VCF.Filter: interactive prioritization of disease-linked genetic variants from sequencing data , 2017, Nucleic Acids Res..
[99] O. Stegle,et al. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning , 2017, Genome Biology.
[100] Irina I. Abnizova,et al. Computational Errors and Biases in Short Read Next Generation Sequencing , 2017 .
[101] Beilun Wang,et al. Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks , 2016, PSB.
[102] Seunghyun Park,et al. Deep Recurrent Neural Network-Based Identification of Precursor microRNAs , 2017, NIPS.
[103] Barnabás Póczos,et al. Predicting enhancer-promoter interaction from genomic sequence with deep neural networks , 2016, bioRxiv.
[104] L. Siu,et al. Approaches to modernize the combination drug development paradigm , 2016, Genome Medicine.
[105] Sharon R Grossman,et al. Systematic mapping of functional enhancer–promoter connections with CRISPR interference , 2016, Science.
[106] Jun Cui,et al. Modeling of signaling crosstalk-mediated drug resistance and its implications on drug combination , 2016, Oncotarget.
[107] Jens Hjerling-Leffler,et al. Disentangling neural cell diversity using single-cell transcriptomics , 2016, Nature Neuroscience.
[108] Yanjun Qi,et al. DeepChrome: deep-learning for predicting gene expression from histone modifications , 2016, Bioinform..
[109] J. McPherson,et al. Coming of age: ten years of next-generation sequencing technologies , 2016, Nature Reviews Genetics.
[110] Seunghyun Park,et al. deepMiRGene: Deep Neural Network based Precursor microRNA Prediction , 2016, ArXiv.
[111] Rachel L. Goldfeder,et al. Medical implications of technical accuracy in genome sequencing , 2016, Genome Medicine.
[112] W. Wasserman,et al. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods , 2016, bioRxiv.
[113] Yanjun Qi,et al. Deep Motif: Visualizing Genomic Sequence Classifications , 2016, ArXiv.
[114] Soumith Chintala,et al. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks , 2015, ICLR.
[115] David R. Kelley,et al. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks , 2015, bioRxiv.
[116] Antonino Fiannaca,et al. A Deep Learning Model for Epigenomic Studies , 2016, 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).
[117] C. Reddy,et al. Transfer learning for class imbalance problems with inadequate data , 2016, Knowledge and Information Systems.
[118] Xiaohui S. Xie,et al. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences , 2015, bioRxiv.
[119] Yi Li,et al. Gene expression inference with deep learning , 2015, bioRxiv.
[120] Insuk Lee,et al. Systematic comparison of variant calling pipelines using gold standard personal exome variants , 2015, Scientific Reports.
[121] O. Troyanskaya,et al. Predicting effects of noncoding variants with deep learning–based sequence model , 2015, Nature Methods.
[122] B. Frey,et al. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning , 2015, Nature Biotechnology.
[123] S. Quake,et al. A survey of human brain transcriptome diversity at the single cell level , 2015, Proceedings of the National Academy of Sciences.
[124] L Li,et al. A New Drug Combinatory Effect Prediction Algorithm on the Cancer Cell Based on Gene Expression and Dose–Response Curve , 2015, CPT: pharmacometrics & systems pharmacology.
[125] M. Ritchie,et al. Methods of integrating data to uncover genotype–phenotype interactions , 2015, Nature Reviews Genetics.
[126] Yoshua Bengio,et al. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling , 2014, ArXiv.
[127] Lawrence A. Donehower,et al. Combinatorial therapy discovery using mixed integer linear programming , 2014, Bioinform..
[128] Jeffrey Dean,et al. Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.
[129] Stefan C. Kremer,et al. Recurrent Neural Networks , 2013, Handbook on Neural Information Processing.
[130] Gabor T. Marth,et al. Haplotype-based variant detection from short-read sequencing , 2012, 1207.3907.
[131] Dong Yu,et al. Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP] , 2011, IEEE Signal Processing Magazine.
[132] M. DePristo,et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. , 2010, Genome research.
[133] Maria P. Pavlou,et al. Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer , 2010, Nature Reviews Cancer.
[134] P. Bork,et al. A method and server for predicting damaging missense mutations , 2010, Nature Methods.
[135] Ernesto Picardi,et al. Bioinformatics approaches for genomics and post genomics applications of next-generation sequencing , 2010, Briefings Bioinform..
[136] Jorge Cortes,et al. Systems approaches and algorithms for discovery of combinatorial therapies. , 2009, Wiley interdisciplinary reviews. Systems biology and medicine.
[137] A. Barabasi,et al. Network medicine : a network-based approach to human disease , 2010 .
[138] Gonçalo R. Abecasis,et al. The Sequence Alignment/Map format and SAMtools , 2009, Bioinform..
[139] Simon Kasif,et al. Probabilistic Protein Function Prediction from Heterogeneous Genome-Wide Data , 2007, PloS one.
[140] Russ Altman,et al. Pharmacogenomics: Challenges and Opportunities , 2006, Annals of Internal Medicine.
[141] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[142] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[143] D. Mercola,et al. The transcription factor Egr1 is a direct regulator of multiple tumor suppressors including TGFβ1, PTEN, p53, and fibronectin , 2006, Cancer Gene Therapy.
[144] F. Scarselli,et al. A new model for learning in graph domains , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[145] S. Batzoglou,et al. Distribution and intensity of constraint in mammalian genomic sequence. , 2005, Genome research.
[146] A. Bezerianos,et al. Gene Networks Inference From Expression Data Using a Recurrent Neuro-Fuzzy Approach , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.
[147] J. Woodcock,et al. Translation of pharmacogenomics and pharmacogenetics: a regulatory perspective , 2004, Nature Reviews Drug Discovery.
[148] 森村 浩季,et al. IEEE International Solid-State Circuits Conference (ISSCC) 2004 国際会議報告 , 2004 .
[149] B. Honoré,et al. Functional genomics studied by proteomics. , 2004, BioEssays : news and reviews in molecular, cellular and developmental biology.
[150] Kunihiko Fukushima,et al. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.
[151] Charles Auffray,et al. From functional genomics to systems biology: concepts and practices. , 2003, Comptes rendus biologies.
[152] Steven Henikoff,et al. SIFT: predicting amino acid changes that affect protein function , 2003, Nucleic Acids Res..
[153] Hilde van der Togt,et al. Publisher's Note , 2003, J. Netw. Comput. Appl..
[154] Michael R. Green,et al. Expressing the human genome , 2001, Nature.
[155] Frank H. Guenther,et al. Neural Networks: Biological Models and Applications , 2001 .
[156] R. E. White,et al. High-throughput screening in drug metabolism and pharmacokinetic support of drug discovery. , 2000, Annual review of pharmacology and toxicology.
[157] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[158] Kuldip K. Paliwal,et al. Bidirectional recurrent neural networks , 1997, IEEE Trans. Signal Process..
[159] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[160] G. Schuler,et al. Entrez: molecular biology database and retrieval system. , 1996, Methods in enzymology.
[161] Andreas Zell,et al. Simulation neuronaler Netze , 1994 .
[162] Jacek M. Zurada,et al. Introduction to artificial neural systems , 1992 .
[163] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[164] Kunihiko Fukushima,et al. Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition , 1982 .
[165] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.