Artificial Intelligence in Cancer Research and Precision Medicine

Deep learning

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

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., Wilson, C. J., Lehár, J., Kryukov, G. V., Sonkin, D., et al. (2012) · Nature · doi:10.1038/nature11003 · Cited by 7,950 · see citing works

Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells

Yang, W., Soares, J., Greninger, P., Edelman, E. J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J. A., Thompson, I. R., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1111 · Cited by 4,051 · see citing works

A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles

Subramanian, A., Narayan, R., Corsello, S. M., Peck, D. D., Natoli, T. E., Lu, X., Gould, J., Davis, J. F., Tubelli, A. A., Asiedu, J. K., et al. (2017) · Cell · doi:10.1016/j.cell.2017.10.049 · Cited by 3,515 · see citing works

Genotypic and Histological Evolution of Lung Cancers Acquiring Resistance to EGFR Inhibitors

Sequist, L. V., Waltman, B. A., Dias-Santagata, D., Digumarthy, S., Turke, A. B., Fidias, P., Bergethon, K., Shaw, A. T., Gettinger, S., Cosper, A. K., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002003 · Cited by 3,499 · see citing works

Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer

Ehteshami Bejnordi, B., Veta, M., Johannes van Diest, P., van Ginneken, B., Karssemeijer, N., Litjens, G., van der Laak, J. A. W. M., and the CAMELYON16 Consortium, Hermsen, M., Manson, Q. F., et al. (2017) · JAMA · doi:10.1001/jama.2017.14585 · Cited by 3,293 · cited 1× · see citing works

International evaluation of an AI system for breast cancer screening

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. S., Darzi, A., et al. (2020) · Nature · doi:10.1038/s41586-019-1799-6 · Cited by 3,287 · see citing works

Defining a Cancer Dependency Map

Tsherniak, A., Vazquez, F., Montgomery, P. G., Weir, B. A., Kryukov, G., Cowley, G. S., Gill, S., Harrington, W. F., Pantel, S., Krill-Burger, J. M., et al. (2017) · Cell · doi:10.1016/j.cell.2017.06.010 · Cited by 3,276 · see citing works

DNA methylation-based classification of central nervous system tumours

Capper, D., Jones, D., Sill, M., Hovestadt, V., Schrimpf, D., Sturm, D., Koelsche, C., Sahm, F., Chávez, L., Reuß, D., et al. (2018) · Nature · doi:10.1038/nature26000 · Cited by 3,046 · see citing works

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018) · Nature Medicine · doi:10.1038/s41591-018-0177-5 · Cited by 2,930 · see citing works

Clinical-grade computational pathology using weakly supervised deep learning on whole slide images

Campanella, G., Hanna, M. G., Geneslaw, L., Miraflor, A., Werneck Krauss Silva, V., Busam, K. J., Brogi, E., Reuter, V. E., Klimstra, D. S., & Fuchs, T. J. (2019) · Nature Medicine · doi:10.1038/s41591-019-0508-1 · Cited by 2,799 · see citing works

Detection and localization of surgically resectable cancers with a multi-analyte blood test

Cohen, J. D., Li, L., Wang, Y., Thoburn, C., Afsari, B., Danilova, L., Douville, C., Javed, A. A., Wong, F., Mattox, A., et al. (2018) · Science · doi:10.1126/science.aar3247 · Cited by 2,783 · see citing works

Development of tumor mutation burden as an immunotherapy biomarker: utility for the oncology clinic

Chan, T. A., Yarchoan, M., Jaffee, E., Swanton, C., Quezada, S. A., Stenzinger, A., & Peters, S. (2019) · Annals of Oncology · doi:10.1093/annonc/mdy495 · Cited by 2,683 · see citing works

The NCI60 human tumour cell line anticancer drug screen

Shoemaker, R. H. (2006) · Nature reviews. Cancer · doi:10.1038/nrc1951 · Cited by 2,644 · see citing works

The human cell atlas

Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C., Birney, E., Bodenmiller, B., Campbell, P., Carninci, P., Clatworthy, M., et al. (2017) · eLife · doi:10.7554/elife.27041 · Cited by 2,232 · see citing works

A Landscape of Pharmacogenomic Interactions in Cancer

Iorio, F., Knijnenburg, T. A., Vis, D. J., Bignell, G. R., Menden, M. P., Schubert, M., Aben, N., Gonçalves, E., Barthorpe, S., Lightfoot, H., et al. (2016) · Cell · doi:10.1016/j.cell.2016.06.017 · Cited by 2,090 · see citing works

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 · see citing works

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458 · see citing works

Deep reinforcement learning for de novo drug design

Popova, M., Isayev, O., & Tropsha, A. (2018) · Science Advances · doi:10.1126/sciadv.aap7885 · Cited by 1,379 · see citing works

Molecular de-novo design through deep reinforcement learning

Olivecrona, M., Blaschke, T., Engkvist, O., & Chen, H. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0235-x · Cited by 1,358 · see citing works

Lung pattern classification for interstitial lung diseases using a deep convolutional neural network

Anthimopoulos, M., Christodoulidis, S., Ebner, L., Christe, A., & Mougiakakou, S. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2535865 · Cited by 1,343 · see citing works

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 · see citing works

Predicting cancer outcomes from histology and genomics using convolutional networks

Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Velázquez Vega, J. E., Brat, D. J., & Cooper, L. A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1717139115 · Cited by 1,084 · see citing works

Enhanced detection of circulating tumor DNA by fragment size analysis

Mouliere, F. C., Chandrananda, D., Piskorz, A., Moore, E. K., Morris, J., Ahlborn, L. B., Mair, R. J., Goranova, T., Marass, F., Heider, K., et al. (2018) · Science Translational Medicine · doi:10.1126/scitranslmed.aat4921 · Cited by 1,071 · see citing works

Integrative molecular and clinical modeling of clinical outcomes to PD1 blockade in patients with metastatic melanoma

Liu, D., Schilling, B., Liu, D., Sucker, A., Livingstone, E., Jerby-Arnon, L., Zimmer, L., Gutzmer, R., Satzger, I., Loquai, C., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0654-5 · Cited by 1,039 · see citing works

Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images

Saltz, J., Gupta, R., Hou, L., Kurc, T., Singh, P., Nguyen, V., Samaras, D., Shroyer, K. R., Zhao, T., Batiste, R., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.03.086 · Cited by 1,027 · see citing works

Meta-analysis of tumor- and T cell-intrinsic mechanisms of sensitization to checkpoint inhibition

Litchfield, K., Reading, J. L., Puttick, C., Thakkar, K., Abbosh, C., Bentham, R., Watkins, T. B. K., Rosenthal, R., Biswas, D., Rowan, A., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.002 · Cited by 893 · see citing works

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.26 · Cited by 892 · see citing works

Discovering the anticancer potential of non-oncology drugs by systematic viability profiling

Corsello, S. M., Nagari, R. T., Spangler, R. D., Rossen, J., Kocak, M., Bryan, J. G., Humeidi, R., Peck, D., Wu, X., Tang, A. A., et al. (2020) · Nature Cancer · doi:10.1038/s43018-019-0018-6 · Cited by 878 · see citing works

Integrating genomic features for non-invasive early lung cancer detection

Chabon, J. J., Hamilton, E. G., Kurtz, D. M., Esfahani, M. S., Moding, E. J., Stehr, H., Schroers‐Martin, J. G., Nabet, B. Y., Chen, B., Chaudhuri, A. A., et al. (2020) · Nature · doi:10.1038/s41586-020-2140-0 · Cited by 686 · see citing works

Establishment of patient-derived cancer organoids for drug-screening applications

Driehuis, E., Kretzschmar, K., & Clevers, H. C. (2020) · Nature Protocols · doi:10.1038/s41596-020-0379-4 · Cited by 656 · see citing works

Deep learning based tissue analysis predicts outcome in colorectal cancer

Bychkov, D., Linder, N., Turkki, R., Nordling, S., Kovanen, P. E., Verrill, C., Walliander, M., Lundin, M., Haglund, C., & Lundin, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-21758-3 · Cited by 644 · see citing works

Graph Convolutional Policy Network for Goal-Directed Molecular Graph GenerationPreprint

You, J., Liu, B., Ying, R., Pande, V. S., & Leskovec, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.02473 · Cited by 644 · see citing works

Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging

Xu, Y., Hosny, A., Zeleznik, R., Parmar, C., Coroller, T. P., Franco, I. “., Mak, R. H., & Aerts, H. J. W. L. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-2495 · Cited by 635 · see citing works

Deep learning for prediction of colorectal cancer outcome: a discovery and validation study

Skrede, O.-J., De Raedt, S., Kleppe, A., Hveem, T. S., Liestøl, K., Maddison, J., Askautrud, H. A., Pradhan, M., Nesheim, J. A., Albregtsen, F., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(19)32998-8 · Cited by 627 · see citing works

The Clinical Relevance of Cancer Cell Lines

Gillet, J.-P., Varma, S., & Gottesman, M. M. (2013) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djt007 · Cited by 601 · see citing works

The need for uncertainty quantification in machine-assisted medical decision making

Begoli, E., Bhattacharya, T., & Kusnezov, D. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-018-0004-1 · Cited by 597 · see citing works

Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk

Zhou, J., Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., & Troyanskaya, O. G. (2018) · Nature Genetics · doi:10.1038/s41588-018-0160-6 · Cited by 559 · see citing works

Predicting drug response and synergy using a deep learning model of human cancer cells

Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F., Ma, J., & Ideker, T. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.09.014 · Cited by 544 · see citing works

Transparency and reproducibility in artificial intelligence

Haibe-Kains, B., Adam, G. A., Hosny, A., Khodakarami, F., Massive Analysis Quality Control (MAQC) Society Board of Directors, Shraddha, T., Kusko, R., Sansone, S.-A., Tong, W., Wolfinger, R. D., et al. (2020) · Nature · doi:10.1038/s41586-020-2766-y · Cited by 540 · see citing works

deepDR: a network-based deep learning approach toin silicodrug repositioning

Zeng, X., Zhu, S., Liu, X., Zhou, Y., Nussinov, R., & Cheng, F. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz418 · Cited by 532 · see citing works

Cancer Genome Interpreter annotates the biological and clinical relevance of tumor alterations

Tamborero, D., Rubio-Pérez, C., Deu-Pons, J., Schroeder, M. P., Vivancos, A. P., Rovira, A., Tusquets, I., Albanell, J., Rodon, J. A., Tabernero, J. M., et al. (2018) · Genome Medicine · doi:10.1186/s13073-018-0531-8 · Cited by 523 · see citing works

Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer

Nagpal, K., Foote, D., Liu, Y., Chen, P.-H. C., Wulczyn, E., Tan, F., Olson, N., Smith, J. L., Mohtashamian, A., Wren, J. H., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0112-2 · Cited by 495 · see citing works

MolGAN: An implicit generative model for small molecular graphs

De Cao, N., & Kipf, T. (2018) · arXiv · doi:10.48550/arxiv.1805.11973 · Cited by 442 · see citing works

Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning

Wang, S., Shi, J., Ye, Z., Dong, D., Yu, D., Zhou, M., Liu, Y., Gevaert, O., Wang, K., Zhu, Y., et al. (2019) · European Respiratory Journal · doi:10.1183/13993003.00986-2018 · Cited by 437 · see citing works

Deep learning with multimodal representation for pancancer prognosis prediction

Cheerla, A., & Gevaert, O. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz342 · Cited by 421 · see citing works

Deep Learning Models for Histopathological Classification of Gastric and Colonic Epithelial Tumours

Iizuka, O., Kanavati, F., Kato, K., Rambeau, M., Arihiro, K., & Tsuneki, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-58467-9 · Cited by 404 · see citing works

Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N. H., Peng, L. H., Hipp, J., & Stumpe, M. C. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0147-oa · Cited by 402 · see citing works

Perspective on Oncogenic Processes at the End of the Beginning of Cancer Genomics

Ding, L., Bailey, M. H., Porta-Pardo, E., Thorsson, V., Colaprico, A., Bertrand, D., Gibbs, D. L., Weerasinghe, A., Huang, K.-L., Tokheim, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.03.033 · Cited by 399 · see citing works

Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study

Yamashita, R., Long, J., Longacre, T., Peng, L., Berry, G., Martin, B., Higgins, J., Rubin, D. L., & Shen, J. (2021) · The Lancet Oncology · doi:10.1016/s1470-2045(20)30535-0 · Cited by 391 · see citing works

Molecular Gene Expression Profiling to Predict the Tissue of Origin and Direct Site-Specific Therapy in Patients With Carcinoma of Unknown Primary Site: A Prospective Trial of the Sarah Cannon Research Institute

Hainsworth, J. D., Rubin, M. S., Spigel, D. R., Boccia, R. V., Raby, S., Quinn, R., & Greco, F. A. (2012) · Journal of Clinical Oncology · doi:10.1200/jco.2012.43.3755 · Cited by 365 · see citing works

Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen

Menden, M. P., Wang, D. Y. Q., Mason, M. J., Szalai, B., Bulusu, K. C., Guan, Y., Yu, T. V., Kang, J., Jeon, M., Wolfinger, R. D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09799-2 · Cited by 360 · see citing works

mHealth 2.0: Experiences, Possibilities, and Perspectives

Becker, S., Miron-Shatz, T., Schumacher, N., Krocza, J., Diamantidis, C., & Albrecht, U.-V. (2014) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3328 · Cited by 354 · see citing works

Estimation of ADME Properties with Substructure Pattern Recognition

Shen, J., Cheng, F., Xu, Y., Li, W., & Tang, Y. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100104j · Cited by 351 · see citing works

Deep learning for classification of colorectal polyps on whole-slide images

Korbar, B., Olofson, A. M., Miraflor, A. P., Nicka, C. M., Suriawinata, M. A., Torresani, L., Suriawinata, A. A., & Hassanpour, S. (2017) · Journal of Pathology Informatics · doi:10.4103/jpi.jpi_34_17 · Cited by 349 · see citing works

A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data

Sun, D., Wang, M., & Li, A. (2019) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2806438 · Cited by 338 · see citing works

A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling

Martins, I. F. D. S., Teixeira, A. L., Pinheiro, L., & Falcão, A. O. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300124c · Cited by 336 · see citing works

Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images

Khosravi, P., Kazemi, E., Imielinski, M., Elemento, O., & Hajirasouliha, I. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2017.12.026 · Cited by 334 · see citing works

Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature

Chang, Y., Park, H., Yang, H., Lee, S., Lee, K.-Y., Kim, T. S., Jung, J., & Shin, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-27214-6 · Cited by 319 · see citing works

A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials

Gayvert, K. M., Madhukar, N. S., & Elemento, O. (2016) · Cell chemical biology · doi:10.1016/j.chembiol.2016.07.023 · Cited by 310 · see citing works

Non-invasive decision support for NSCLC treatment using PET/CT radiomics

Mu, W., Jiang, L., Zhang, J., Shi, Y., Gray, J. E., Tunali, I., Gao, C., Sun, Y., Tian, J., Zhao, X., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19116-x · Cited by 289 · see citing works

Mol-CycleGAN: a generative model for molecular optimization

Maziarka, Ł., Pocha, A., Kaczmarczyk, J., Rataj, K., Danel, T., & Warchoł, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-019-0404-1 · Cited by 275 · see citing works

A Bayesian machine learning approach for drug target identification using diverse data types

Madhukar, N. S., Khade, P. K., Huang, L., Gayvert, K. M., Galletti, G., Stogniew, M., Allen, J. E., Giannakakou, P., & Elemento, O. (2019) · Nature Communications · doi:10.1038/s41467-019-12928-6 · Cited by 267 · see citing works

Convolutional neural network models for cancer type prediction based on gene expression

Mostavi, M., Chiu, Y., Huang, Y., & Chen, Y. (2020) · BMC Medical Genomics · doi:10.1186/s12920-020-0677-2 · Cited by 266 · see citing works

Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies

Freeman, K., Dinnes, J., Chuchu, N., Takwoingi, Y., Bayliss, S. E., Matin, R. N., Jain, A., Walter, F. M., Williams, H. C., & Deeks, J. J. (2020) · BMJ · doi:10.1136/bmj.m127 · Cited by 260 · see citing works

Deep learning–based cell composition analysis from tissue expression profiles

Menden, K., Marouf, M., Oller, S., Dalmia, A., Magruder, D. S., Kloiber, K., Heutink, P., & Bonn, S. (2020) · Science Advances · doi:10.1126/sciadv.aba2619 · Cited by 258 · see citing works

Integrated Analysis of Genetic Ancestry and Genomic Alterations across Cancers

Yuan, J., Hu, Z., Mahal, B. A. V., Zhao, S. D., Kensler, K. H., Pi, J., Hu, X., Zhang, Y., Wang, Y., Jiang, J., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.08.019 · Cited by 258 · see citing works

Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning

Chen, M., Zhang, B., Topatana, W., Cao, J., Zhu, H.-P., Juengpanich, S., Mao, Q., Yu, H., & Cai, X. (2020) · npj Precision Oncology · doi:10.1038/s41698-020-0120-3 · Cited by 246 · see citing works

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

Chiu, Y., Chen, H.-I. H., Zhang, T., Zhang, S., Gorthi, A., Wang, L., Huang, Y., & Chen, Y. (2019) · BMC Medical Genomics · doi:10.1186/s12920-018-0460-9 · Cited by 227 · see citing works

CURATE.AI: Optimizing Personalized Medicine with Artificial Intelligence

Blasiak, A., Khong, J., & Kee, T. W. (2019) · SLAS TECHNOLOGY · doi:10.1177/2472630319890316 · Cited by 225 · see citing works

Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies

Bejnordi, B. E., Mullooly, M., Pfeiffer, R. M., Fan, S., Vacek, P. M., Weaver, D. L., Herschorn, S. D., Brinton, L. A., van Ginneken, B., Karssemeijer, N., et al. (2018) · Modern Pathology · doi:10.1038/s41379-018-0073-z · Cited by 218 · see citing works

A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns

Jiao, W., Atwal, G., Polak, P. P., Karlić, R., Cuppen, E. P. J. G., Al‐Shahrour, F., Atwal, G., Bailey, P. J., Biankin, A. V., Boutros, P. C., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13825-8 · Cited by 216 · see citing works

Deep representation learning of electronic health records to unlock patient stratification at scale

Landi, I., Glicksberg, B. S., Lee, H.-C., Cherng, S., Landi, G., Danieletto, M., Dudley, J. T., Furlanello, C., & Miotto, R. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0301-z · Cited by 213 · see citing works

Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets

Chen, B., Ma, L., Paik, H., Sirota, M., Wei, W., Chua, M., So, S. K., & Butte, A. J. (2017) · Nature Communications · doi:10.1038/ncomms16022 · Cited by 209 · see citing works

A Deep Learning Framework for Predicting Response to Therapy in Cancer

Sakellaropoulos, T., Vougas, K. N., Narang, S., Koinis, F., Kotsinas, A., Polyzos, A., Moss, T. J., Piha-Paul, S. A., Zhou, H., Kardala, E., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.017 · Cited by 204 · see citing works

Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status

Korfiatis, P. D., Kline, T. L., Lachance, D. H., Parney, I. F., Buckner, J. C., & Erickson, B. J. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-0009-z · Cited by 200 · see citing works

Influence of Chemotherapy on <i>EGFR</i> Mutation Status Among Patients With Non–Small-Cell Lung Cancer

Bai, H., Wang, Z., Chen, K., Zhao, J., Lee, J. J., Wang, S., Zhou, Q., Zhuo, M., Mao, L., An, T., et al. (2012) · Journal of Clinical Oncology · doi:10.1200/jco.2011.39.3744 · Cited by 199 · see citing works

Searching for prostate cancer by fully automated magnetic resonance imaging classification: deep learning versus non-deep learning

Wang, X., Yang, W. H., Weinreb, J. C., Han, J., Li, Q., Kong, X., Yan, Y., Ke, Z., Luo, B., Liu, T., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-15720-y · Cited by 196 · see citing works

Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Johannet, P. M., Coudray, N., Donnelly, D. M., Jour, G., Illa-Bochaca, I., Xia, Y., Johnson, D. B., Wheless, L., Patrinely, J. R., Nomikou, S., et al. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-20-2415 · Cited by 181 · see citing works

Evaluation of Nine Somatic Variant Callers for Detection of Somatic Mutations in Exome and Targeted Deep Sequencing Data

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Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker

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