Deep Learning in Pharmacogenomics: From Gene Regulation to Patient Stratification

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126

A global reference for human genetic variation

The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247

An integrated encyclopedia of DNA elements in the human genome

The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332

Integrative analysis of 111 reference human epigenomes

Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368

The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets

Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309

Predicting the future - big data, machine learning, and clinical medicine

Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652

MoleculeNet: a benchmark for molecular machine learning

Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873

Learning Important Features Through Propagating Activation Differences

Shrikumar, A., Greenside, P., & Kundaje, A. (2017) · arXiv · doi:10.48550/arxiv.1704.02685 · Cited by 2,761

Predicting effects of noncoding variants with deep learning–based sequence model

Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310

A universal SNP and small-indel variant caller using deep neural networks

Poplin, R., Chang, P.-C., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4235 · Cited by 1,840

The Unreasonable Effectiveness of Data

Halevy, A. Y., Norvig, P., & Pereira, F. M. B. (2009) · IEEE Intelligent Systems · doi:10.1109/mis.2009.36 · Cited by 1,776

Deep patient: An unsupervised representation to predict the future of patients from the electronic health records

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2767063 · Cited by 1,688

Deep learning for computational biology

Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473

Deep learning-based multi-omics integration robustly predicts survival in liver cancer

Chaudhary, K., Poirion, O. B., Lu, L., & Garmire, L. X. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-0853 · Cited by 1,091

DeepTox: toxicity prediction using deep learning

Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

Kelley, D. R., Snoek, J., & Rinn, J. L. (2016) · Genome Research · doi:10.1101/gr.200535.115 · Cited by 1,048

Cardiologist-Level Arrhythmia Detection with Convolutional Neural NetworksPreprint

Rajpurkar, P., Hannun, A., Haghpanahi, M., Bourn, C., & Ng, A. Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.01836 · Cited by 970

The 3D Genome as Moderator of Chromosomal Communication

Dekker, J., & Mirny, L. (2016) · Cell · doi:10.1016/j.cell.2016.02.007 · Cited by 937

Patterns of treatment response in newly diagnosed epilepsy

Brodie, M. J., Barry, S., Bamagous, G. A., Norrie, J. D., & Kwan, P. (2012) · Neurology · doi:10.1212/wnl.0b013e3182563b19 · Cited by 931

Doctor AI: Predicting Clinical Events via Recurrent Neural Networks

Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2015) · Europe PMC (PubMed Central) · doi:10.48550/arxiv.1511.05942 · Cited by 873

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

Quang, D. X., & Xie, X. S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw226 · Cited by 866

Deep learning for computational chemistry

Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809

Low Data Drug Discovery with One-Shot Learning

Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.6b00367 · Cited by 799

Model selection and overfitting

Lever, J., Krzywinski, M. I., & Altman, N. (2016) · Nature Methods · doi:10.1038/nmeth.3968 · Cited by 727

Deep Learning in Drug Discovery

Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723

Applications of Deep Learning in Biomedicine

Mamoshina, P., Vieira, A., Lane, E., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5b00982 · Cited by 701

DeepSynergy: predicting anti-cancer drug synergy with Deep Learning

Preuer, K., Lewis, R. P. I., Hochreiter, S., Bender, A., Bulusu, K. C., & Klambauer, G. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx806 · Cited by 668

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6b00248 · Cited by 610

Deep-Learning-Based Drug–Target Interaction Prediction

Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., & Lü, H. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.6b00618 · Cited by 592

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A. (2017) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.7b00346 · Cited by 587

Big Data Analytics in Healthcare

Belle, A., Thiagarajan, R., Soroushmehr, S. M. R., Navidi, F., Beard, D. A., & Najarian, K. (2015) · BioMed Research International · doi:10.1155/2015/370194 · Cited by 580

Untitled work

(2016) · PubMed · Cited by 576

Sequential regulatory activity prediction across chromosomes with convolutional neural networks

Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018) · Genome Research · doi:10.1101/gr.227819.117 · Cited by 537

DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017) · Genome Biology · doi:10.1186/s13059-017-1189-z · Cited by 536

Improving palliative care with deep learning

Avati, A., Jung, K., Harman, S. M., Downing, N. L., Ng, A. Y., & Shah, N. H. (2017) · BMC Medical Informatics and Decision Making · doi:10.1109/bibm.2017.8217669 · Cited by 472

Classification evaluation

Lever, J., Krzywinski, M. I., & Altman, N. (2016) · Nature Methods · doi:10.1038/nmeth.3945 · Cited by 417

Application of Generative Autoencoder in De Novo Molecular Design

Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J., & Chen, H. (2018) · Molecular Informatics · doi:10.1002/minf.201700123 · Cited by 411

Deep Learning for Drug-Induced Liver Injury

Xu, Y., Dai, Z., Chen, F., Gao, S., Pei, J., & Lai, L. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00238 · Cited by 405

Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning

Shvets, A. A., Rakhlin, A., Kalinin, A. A., & Iglovikov, V. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00100 · Cited by 402

The cornucopia of meaningful leads: Applying deep adversarial autoencoders for new molecule development in oncology

Kadurin, A., Aliper, A., Kazennov, A., Mamoshina, P., Vanhaelen, Q., Khrabrov, K., & Zhavoronkov, A. (2017) · Oncotarget · doi:10.18632/oncotarget.14073 · Cited by 350

Opportunities and obstacles for deep learning in biology and medicinePreprint

Ching, T. H., Himmelstein, D. S., Beaulieu‐Jones, B. K., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/142760 · Cited by 308

DeepChrome: deep-learning for predicting gene expression from histone modifications

Singh, R., Lanchantin, J., Robins, G., & Qi, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw427 · Cited by 307

Models of human core transcriptional regulatory circuitriesPreprint

Saint‐André, V., Alexander Joel Federation, Lin, C. Y., Abraham, B. J., Reddy, J., Lee, T. I., Bradner, J. E., & Young, R. A. (2016) · Genome Research · doi:10.1101/gr.197590.115 · Cited by 303

High-resolution interrogation of functional elements in the noncoding genome

Sanjana, N. E., Wright, J. B., Zheng, K., Shalem, O., Fontanillas, P., Joung, J., Cheng, C. S., Regev, A., & Zhang, F. (2016) · Science · doi:10.1126/science.aaf7613 · Cited by 290

Patient-centric trials for therapeutic development in precision oncology

Biankin, A. V., Piantadosi, S., & Hollingsworth, S. J. (2015) · Nature · doi:10.1038/nature15819 · Cited by 286

Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets

Leung, M. K. K., Delong, A., Alipanahi, B., & Frey, B. J. (2015) · Proceedings of the IEEE · doi:10.1109/jproc.2015.2494198 · Cited by 284

Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks

Umarov, R. K., & Solovyev, V. V. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171410 · Cited by 276

DeepCare: A Deep Dynamic Memory Model for Predictive Medicine

Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-31750-2_3 · Cited by 269

ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions

Sushko, I., Salmina, E. S., Potemkin, V., Poda, G. I., & Tetko, I. V. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300245q · Cited by 265

Is Multitask Deep Learning Practical for Pharma?

Ramsundar, B., Liu, B., Wu, Z., Verras, A., Tudor, M., Sheridan, R. P., & Pande, V. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00146 · Cited by 263

Extracting research-quality phenotypes from electronic health records to support precision medicine

Wei, W., & Denny, J. C. (2015) · Genome Medicine · doi:10.1186/s13073-015-0166-y · Cited by 251

Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction

Xu, Y., Pei, J., & Lai, L. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00244 · Cited by 250

Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks

Pan, X., Rijnbeek, P. R., Yan, J., & Shen, H. (2018) · BMC Genomics · doi:10.1186/s12864-018-4889-1 · Cited by 249

A renaissance of neural networks in drug discovery

Baskin, I. I., Winkler, D., & Tetko, I. V. (2016) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2016.1201262 · Cited by 235

Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach

Liang, M., Li, Z., Chen, T., & Zeng, J. (2015) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2377729 · Cited by 231

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

Quang, D., & Xie, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.03.020 · Cited by 204

RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach

Pan, X., & Shen, H. (2017) · BMC Bioinformatics · doi:10.1186/s12859-017-1561-8 · Cited by 200

Optimal medication dosing from suboptimal clinical examples: a deep reinforcement learning approach

Nemati, S., Ghassemi, M. M., & Clifford, G. D. (2016) · 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591355 · Cited by 195

Predicting enhancer‐promoter interaction from genomic sequence with deep neural networks

Singh, S. K., Yang, Y., Póczos, B., & Ma, J. (2019) · Quantitative Biology · doi:10.1007/s40484-019-0154-0 · Cited by 176

Semi-supervised learning of the electronic health record for phenotype stratification

Beaulieu‐Jones, B. K., & Greene, C. S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.10.007 · Cited by 175

Deep learning for regulatory genomics

Park, Y., & Kellis, M. (2015) · Nature Biotechnology · doi:10.1038/nbt.3313 · Cited by 169

A vision and strategy for the virtual physiological human in 2010 and beyond

Hunter, P. J., Coveney, P. V., de Bono, B., Díaz‐Zuccarini, V., Fenner, J. W., Frangi, A. F., Harris, P. J. F., Hose, R., Kohl, P. K., Lawford, P. V., et al. (2010) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2010.0048 · Cited by 160

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

Dinov, I. D. (2016) · GigaScience · doi:10.1186/s13742-016-0117-6 · Cited by 153

MiRFinder: an improved approach and software implementation for genome-wide fast microRNA precursor scans

Huang, T., Fan, B., Rothschild, M. F., Hu, Z., Li, K., & Zhao, S. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-341 · Cited by 147

Does Pharmacogenomic Testing Improve Clinical Outcomes for Major Depressive Disorder?: A Systematic Review of Clinical Trials and Cost-Effectiveness Studies

Rosenblat, J. D., Lee, Y., & McIntyre, R. S. (2017) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.15r10583 · Cited by 138

DEEP MOTIF DASHBOARD: VISUALIZING AND UNDERSTANDING GENOMIC SEQUENCES USING DEEP NEURAL NETWORKS

Lanchantin, J., Singh, R., Wang, B., & Qi, Y. (2016) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813207813_0025 · Cited by 130

Chromatin accessibility prediction via convolutional long short-term memory networks withk-mer embedding

Xu, M., Zeng, W., Chen, N., Chen, T., & Jiang, R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx234 · Cited by 126

Deep Convolutional Neural Networks for Breast Cancer Histology Image AnalysisPreprint

Rakhlin, A., Shvets, A. A., Iglovikov, V., & Kalinin, A. A. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/259911 · Cited by 116

Pharmacogenomics in clinical practice and drug development

Harper, A. R., & Topol, E. J. (2012) · Nature Biotechnology · doi:10.1038/nbt.2424 · Cited by 109

Case-based reasoning using electronic health records efficiently identifies eligible patients for clinical trials

Miotto, R., & Weng, C. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocu050 · Cited by 104

Clinical Success of Drug Targets Prospectively Predicted by In Silico Study

Zhu, F., Li, X. X., Yang, S. Y., & Chen, Y. (2017) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2017.12.002 · Cited by 102

PEDLA: predicting enhancers with a deep learning-based algorithmic framework

Liu, F., Li, H., Ren, C., Bo, X., & Shu, W. (2016) · Scientific Reports · doi:10.1038/srep28517 · Cited by 101

Predicting the impact of non-coding variants on DNA methylation

Zeng, H., & Gifford, D. K. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx177 · Cited by 101

Imputation for transcription factor binding predictions based on deep learning

Qin, Q., & Feng, J. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005403 · Cited by 96

Creating a universal SNP and small indel variant caller with deep neural networksPreprint

Poplin, R. E., Chang, P.-C., Alexander, D. H., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/092890 · Cited by 94

Modeling Industrial ADMET Data with Multitask NetworksPreprint

Kearnes, S. M., Goldman, B., & Pande, V. S. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1606.08793 · Cited by 92

Identifying Predictive Features in Drug Response Using Machine Learning: Opportunities and Challenges

Vidyasagar, M. (2014) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-010814-124502 · Cited by 87

Mining the Unknown: Assigning Function to Noncoding Single Nucleotide Polymorphisms

Nishizaki, S. S., & Boyle, A. P. (2016) · Trends in Genetics · doi:10.1016/j.tig.2016.10.008 · Cited by 84

Pediatric Bone Age Assessment Using Deep Convolutional Neural NetworksPreprint

Iglovikov, V., Rakhlin, A., Kalinin, A. A., & Shvets, A. A. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/234120 · Cited by 71

Genomics and transcriptomics in drug discovery

Dopazo, J. (2013) · Drug Discovery Today · doi:10.1016/j.drudis.2013.06.003 · Cited by 69

Machine learning-based prediction of adverse drug effects: An example of seizure-inducing compounds

Gao, M., Igata, H., Takeuchi, A., Sato, K., & Ikegaya, Y. (2017) · Journal of Pharmacological Sciences · doi:10.1016/j.jphs.2017.01.003 · Cited by 61

Sequential regulatory activity prediction across chromosomes with convolutional neural networksPreprint

Kelley, D. R., Reshef, Y., Bileschi, M. L., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/161851 · Cited by 60

Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecturePreprint

Schreiber, J. M., Libbrecht, M. W., Bilmes, J. A., & Noble, W. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/103614 · Cited by 59

Mapping Patient Trajectories using Longitudinal Extraction and Deep Learning in the MIMIC-III Critical Care Database

Beaulieu‐Jones, B. K., Orzechowski, P., & Moore, J. H. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0012 · Cited by 58

Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep LearningPreprint

Shvets, A. A., Rakhlin, A., Kalinin, A. A., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/275867 · Cited by 56

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm

Kim, S., Harwani, M., Grama, A. Y., & Chaterji, S. (2016) · Scientific Reports · doi:10.1038/srep38433 · Cited by 53

Cancer pharmacogenomics, challenges in implementation, and patient-focused perspectives

Patel, J. N. (2016) · Pharmacogenomics and Personalized Medicine · doi:10.2147/pgpm.s62918 · Cited by 50

Statistical analysis of big data on pharmacogenomics

B, J. F. A., & Liu, H. (2013) · Advanced Drug Delivery Reviews · doi:10.1016/j.addr.2013.04.008 · Cited by 47

Predicting Enhancer-Promoter Interaction from Genomic Sequence with Deep Neural NetworksPreprint

Singh, S. K., Yang, Y., Póczos, B., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/085241 · Cited by 47

A Glutamatergic Network Mediates Lithium Response in Bipolar Disorder as Defined by Epigenome Pathway Analysis

Higgins, G. A., Allyn-Feuer, A., Barbour, E., & Athey, B. D. (2015) · Pharmacogenomics · doi:10.2217/pgs.15.106 · Cited by 46

DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome SequencingPreprint

Guo, W., Xu, Y. (., & Feng, X. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/135574 · Cited by 43

In-solution hybrid capture of bisulfite-converted DNA for targeted bisulfite sequencing of 174 ADME genes

Ivanov, M., Kals, M., Kacevska, M., Metspalu, A., Ingelman‐Sundberg, M., & Milani, L. A. (2013) · Nucleic Acids Research · doi:10.1093/nar/gks1467 · Cited by 42

A Deep Learning Approach to Detect SNP Interactions

Uppu, S., Krishna, A., & Gopalan, R. P. (2016) · Journal of Software · doi:10.17706/jsw.11.10.965-975 · Cited by 38

Integration of genomics into the electronic health record: mapping terra incognita

Kannry, J. L., & Williams, M. S. (2013) · Genetics in Medicine · doi:10.1038/gim.2013.102 · Cited by 37

CNNsite: Prediction of DNA-binding residues in proteins using Convolutional Neural Network with sequence features

Zhou, J., Lu, Q., Xu, R., Gui, L., & Wang, H. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822496 · Cited by 36

FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential dataPreprint

Quang, D. X., & Xie, X. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/151274 · Cited by 33

Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks

Avsec, Ž., Barekatain, M., Cheng, J., & Gagneur, J. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx727 · Cited by 33

18O-assisted dynamic metabolomics for individualized diagnostics and treatment of human diseases

Nemutlu, E., Zhang, S., Juranić, N., Terzic, A., Macura, S. I., & Dzeja, P. P. (2012) · Croatian Medical Journal · doi:10.3325/cmj.2012.53.529 · Cited by 32

Network Reconstruction Reveals that Valproic Acid Activates Neurogenic Transcriptional Programs in Adult Brain Following Traumatic Injury

Higgins, G. A., Georgoff, P. E., Nikolian, V. C., Allyn-Feuer, A., Pauls, B., Higgins, R., Athey, B. D., & Alam, H. E. (2017) · Pharmaceutical Research · doi:10.1007/s11095-017-2130-6 · Cited by 29

Adversarial Training for Disease Prediction from Electronic Health Records with Missing DataPreprint

Hwang, U., Choi, S., Lee, H. B., & Yoon, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.04126 · Cited by 26

SOCR data dashboard: an integrated big data archive mashing medicare, labor, census and econometric information

Husain, S. S., Kalinin, A. A., Truong, A., & Dinov, I. D. (2015) · Journal Of Big Data · doi:10.1186/s40537-015-0018-z · Cited by 24

Causal Phenotype Discovery via Deep Networks.

Kale, D. C., Che, Z., Bahadori, M. T., Li, W., Liu, Y., & Wetzel, R. C. (2015) · PubMed Central · Cited by 24

Deep Learning and Association Rule Mining for Predicting Drug Response in Cancer. A Personalised Medicine ApproachPreprint

Vougas, K. N., Krochmal, M., Jackson, T., Polyzos, A., Aggelopoulos, A., Pateras, I. S., Liontos, M., Varvarigou, A. A., Johnson, E. O., Georgoulias, V., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/070490 · Cited by 24

Boosting Deep Learning Risk Prediction with Generative Adversarial Networks for Electronic Health RecordsPreprint

Che, Z., Cheng, Y., Zhai, S., Sun, Z., & Liu, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.01648 · Cited by 24

Epigenomic Mapping and Effect Sizes of Noncoding Variants Associated with Psychotropic Drug Response

Higgins, G. A., Allyn-Feuer, A., & Athey, B. D. (2015) · Pharmacogenomics · doi:10.2217/pgs.15.105 · Cited by 23

DL-ADR: a novel deep learning model for classifying genomic variants into adverse drug reactions

Liang, Z., Huang, J. X., Zeng, X., & Zhang, G. (2016) · BMC Medical Genomics · doi:10.1186/s12920-016-0207-4 · Cited by 23

3D Cell Nuclear Morphology: Microscopy Imaging Dataset and Voxel-Based Morphometry Classification Results

Kalinin, A. A., Allyn-Feuer, A., Ade, A. S., Fon, G.-V., Meixner, W., Dilworth, D., de Wet, J. R., Higgins, G. A., Zheng, G., Creekmore, A. L., et al. (2018) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2018.00304 · Cited by 23

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Lanchantin, J., Singh, R., Wang, B., et al. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.03644 · Cited by 22

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Pan, X., Rijnbeek, P. R., Yan, J., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/146175 · Cited by 22

FIDDLE: An integrative deep learning framework for functional genomic data inferencePreprint

Eser, U., & Churchman, L. S. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/081380 · Cited by 19

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Adams, S. M., Conley, Y. P., Wagner, A. K., Jha, R. M., Clark, R. S. B., Poloyac, S. M., Kochanek, P. M., & Empey, P. E. (2017) · Pharmacogenomics · doi:10.2217/pgs-2017-0073 · Cited by 17

DeepEnhancer: Predicting enhancers by convolutional neural networks

Xu, M., Chen, N., Chen, T., & Jiang, R. (2016) · IEEE Conference Proceedings · Cited by 17

Dosing recommendations for pharmacogenetic interactions related to drug metabolism

Filipski, K. K., Pacanowski, M. A., Ramamoorthy, A., Feero, W. G., & Freedman, A. N. (2016) · Pharmacogenetics and Genomics · doi:10.1097/fpc.0000000000000220 · Cited by 15

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Higgins, G. A., Allyn-Feuer, A., Handelman, S. K., Sadée, W., & Athey, B. D. (2015) · Pharmacogenomics · doi:10.2217/pgs.15.111 · Cited by 14

Pharmacogenomics in Cardiology – Genetics and Drug Response: 10 Years of Progress

Cavallari, L. H., & Weitzel, K. W. (2015) · Future Cardiology · doi:10.2217/fca.15.20 · Cited by 14

Virtual Screening: A Challenge for Deep Learning

Pérez-Sianes, J., Pérez‐Sánchez, H., & Díaz, F. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-40126-3_2 · Cited by 12

DeepATAC: A deep-learning method to predict regulatory factor binding activity from ATAC-seq signalsPreprint

Hiranuma, N., Lundberg, S., & Lee, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/172767 · Cited by 12

Methods to Analyze Big Data in Pharmacogenomics Research

Li, R., Kim, D., & Ritchie, M. D. (2017) · Pharmacogenomics · doi:10.2217/pgs-2016-0152 · Cited by 11

Disease Prediction from Electronic Health Records Using Generative Adversarial Networks.Preprint

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Higgins, G. A., Allyn-Feuer, A., Georgoff, P. E., Nikolian, V. C., Alam, H. B., & Athey, B. D. (2017) · Methods · doi:10.1016/j.ymeth.2017.03.012 · Cited by 10

Genetic Architect: Discovering Genomic Structure with Learned Neural ArchitecturesPreprint

Deming, L., Targ, S., Sauder, N., Almeida, D., & Ye, C. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.07156 · Cited by 9

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Kalinin, A. A., Allyn-Feuer, A., Ade, A. S., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/208207 · Cited by 8

Probe Efficient Feature Representation of Gapped K-mer Frequency Vectors from Sequences using Deep Neural NetworksPreprint

Cao, Z., & Zhang, S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/170761 · Cited by 7

Advancing Psychiatric Pharmacogenomics Using Drug Development Paradigms

Smith, R. M. (2017) · Pharmacogenomics · doi:10.2217/pgs-2017-0104 · Cited by 7

Improving Strategy for Discovering Interacting Genetic Variants in Association Studies

Uppu, S., & Krishna, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46687-3_51 · Cited by 5

Rotational 3D mechanogenomic Turing patterns of human colon Caco-2 cells during differentiationPreprint

Zheng, G., Kalinin, A. A., Dinov, I. D., Meixner, W., Zhu, S., & Wiley, J. W. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/272096 · Cited by 3

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310

An overview of deep learning in medical imaging focusing on MRI

Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058

Deep learning: new computational modelling techniques for genomics

Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-019-0122-6 · Cited by 1,288

An overview of deep learning in medical imaging focusing on MRI

Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012

Deep learning in cancer diagnosis, prognosis and treatment selection

Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Waddell, N. (2021) · Genome Medicine · doi:10.1186/s13073-021-00968-x · Cited by 932

The role of machine learning to predict treatment response and target drug therapies

Taylor, C. O., & Stevenson, J. M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824453-1.00002-6

Deep learning of pharmacogenomics resources: moving towards precision oncology

Chiu, Y., Chen, H.-I. H., Gorthi, A., Mostavi, M., Zheng, S., Huang, Y., & Chen, Y. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz144 · Cited by 80

Deep learning: from chemoinformatics to precision medicine

Kim, I., & Oh, J. M. (2017) · Journal of Pharmaceutical Investigation · doi:10.1007/s40005-017-0332-x · Cited by 17

MMDRP: drug response prediction and biomarker discovery using multi-modal deep learning

Taj, F., & Stein, L. D. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae010 · Cited by 12

Machine learning models for pharmacogenomic variant effect predictions - recent developments and future frontiers.

Tremmel, R., Honoré, A., Park, Y., Zhou, Y.-T., Xiao, M., & Lauschke, V. (2025) · Pharmacogenomics (London) · doi:10.1080/14622416.2025.2504863 · Cited by 6