References

Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015). Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning. Nature biotechnology, 33(8), 831-838.

Asch, F. M., Abraham, T., Jankowski, M., Cleve, J., Adams, M., Romano, N., Polivert, N., Hong, H., & Lang, R. (2019). Accuracy and reproducibility of a novel artificial intelligence deep learning-based algorithm for automated calculation of ejection fraction in echocardiography. Journal of the American College of Cardiology, 73(9), 1447. https://doi.org/10.1016/s0735-1097(19)32053-4

Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., Asirvatham, S. J., Scott, C. G., Carter, R. E., & Friedman, P. A. (2018). Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nature Medicine, 25(1), 70–74. https://doi.org/10.1038/s41591-018-0240-2

Baeza-Centurion, P., Miñana, B., Schmiedel, J. M., Valcárcel, J., & Lehner, B. (2019). Combinatorial genetics reveals a scaling law for the effects of mutations on splicing. Cell, 176(3), 549-563.

Bastarache, L., Hughey, J. J., Hebbring, S., Marlo, J., Zhao, W., Ho, W. T., Van Driest, S. L., McGregor, T. L., Mosley, J. D., Wells, Q. S., Temple, M., Ramirez, A. H., Carroll, R., Osterman, T., Edwards, T., Ruderfer, D., Edwards, D. R. V., Hamid, R., Cogan, J., . . . Denny, J. C. (2018). Phenotype risk scores identify patients with unrecognized Mendelian disease patterns. Science, 359(6381), 1233–1239. https://doi.org/10.1126/science.aal4043

Bernstein, B. E., Stamatoyannopoulos, J. A., Costello, J. F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M. A., Beaudet, A. L., Ecker, J. R., Farnham, P. J., Hirst, M., Lander, E. S., Mikkelsen, T. S., & Thomson, J. A. (2010). The NIH Roadmap Epigenomics Mapping Consortium. Nature Biotechnology, 28(10), 1045–1048. https://doi.org/10.1038/nbt1010-1045

Bolukbasi, T., Chang, K., Zou, J. Y., Saligrama, V., & Kalai, A. T. (2016). Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings. https://proceedings.neurips.cc/paper_files/paper/2016/hash/a486cd07e4ac3d270571622f4f316ec5-Abstract.html

Chatterjee, S., & Ahituv, N. (2017). Gene regulatory elements, major drivers of human disease. Annual Review of Genomics and Human Genetics, 18(1), 45–63. https://doi.org/10.1146/annurev-genom-091416-035537

Chen, I. Y., Szolovits, P., & Ghassemi, M. (2019). Can AI help reduce disparities in general medical and mental health care? The AMA Journal of Ethic, 21(2), E167-179. https://doi.org/10.1001/amajethics.2019.167

Chen, J., Druhl, E., Ramesh, B. P., Houston, T. K., Brandt, C. A., Zulman, D. M., Vimalananda, V. G., Malkani, S., & Yu, H. (2018). A natural language processing system that links medical terms in electronic health record notes to lay definitions: system development using physician reviews. Journal of Medical Internet Research, 20(1), e26. https://doi.org/10.2196/jmir.8669

Clark, M. M., Hildreth, A., Batalov, S., Ding, Y., Chowdhury, S., Watkins, K., Ellsworth, K., Camp, B., Kint, C. I., Yacoubian, C., Farnaes, L., Bainbridge, M. N., Beebe, C., Braun, J. J. A., Bray, M., Carroll, J., Cakici, J. A., Caylor, S. A., Clarke, C., . . . Kingsmore, S. F. (2019). Diagnosis of genetic diseases in seriously ill children by rapid whole-genome sequencing and automated phenotyping and interpretation. Science Translational Medicine, 11(489). https://doi.org/10.1126/scitranslmed.aat6177

Collobert, R., & Weston, J. (2008, July). A unified architecture for natural language processing: Deep neural networks with multitasks learning. In Proceedings of the 25th international conference on Machine learning (pp. 160-167).

Coudray, N., Ocampo, P. S., Sakellaropoulos, T., Narula, N., Snuderl, M., Fenyö, D., Moreira, A. L., Razavian, N., & Tsirigos, A. (2018). Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine, 24(10), 1559–1567. https://doi.org/10.1038/s41591-018-0177-5

DePristo, M. A., Banks, E., Poplin, R., Garimella, K. V., Maguire, J. R., Hartl, C., Philippakis, A. A., Del Angel, G., Rivas, M. A., Hanna, M., McKenna, A., Fennell, T. J., Kernytsky, A. M., Sivachenko, A. Y., Cibulskis, K., Gabriel, S. B., Altshuler, D., & Daly, M. J. (2011). A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nature Genetics, 43(5), 491–498. https://doi.org/10.1038/ng.806

Detecting Intracranial Hemorrhage with Deep Learning. (2018, July 1). IEEE Conference Publication | IEEE Xplore. https://ieeexplore.ieee.org/abstract/document/8512336

Dias, R., & Torkamani, A. (2019). Artificial intelligence in clinical and genomic diagnostics. Genome Medicine, 11(1). https://doi.org/10.1186/s13073-019-0689-8

Diller, G., Kempny, A., Babu-Narayan, S. V., Henrichs, M., Brida, M., Uebing, A., Lammers, A. E., Baumgartner, H., Li, W., Wort, S. J., Dimopoulos, K., & Gatzoulis, M. A. (2018). Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients. European Heart Journal, 40(13), 1069–1077. https://doi.org/10.1093/eurheartj/ehy915

Dolgin, E. (2019). AI face-scanning app spots signs of rare genetic disorders. Nature. https://doi.org/10.1038/d41586-019-00027-x

Doshi-Velez, F., & Kim, B. (2017, February 28). Towards a rigorous science of interpretable machine learning. arXiv.org. https://arxiv.org/abs/1702.08608

Edwards, E., Salloum, W., Finley, G. P., Fone, J., Cardiff, G., Miller, M., & Suendermann-Oeft, D. (2017). Medical Speech Recognition: Reaching Parity with Humans. In Lecture notes in computer science (pp. 512–524). https://doi.org/10.1007/978-3-319-66429-3_51

Eraslan, G., Avsec, Ž., Gagneur, J., & Theis, F. J. (2019). Deep learning: new computational modelling techniques for genomics. Nature Reviews Genetics, 20(7), 389–403. https://doi.org/10.1038/s41576-019-0122-6

Erikson, G. A., Bodian, D. L., Rueda, M., Molparia, B., Scott, E. R., Scott-Van Zeeland, A. A., ... & Torkamani, A. (2016). Whole-genome sequencing of a healthy aging cohort. Cell, 165(4), 1002-1011.

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), 115-118.

Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., Cui, C., Corrado, G., Thrun, S., & Dean, J. (2018). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z

Evans, A. J., Bauer, T. W., Bui, M. M., Cornish, T. C., Duncan, H., Glassy, E. F., ... & Pantanowitz, L. (2018). US Food and Drug Administration approval of whole slide imaging for primary diagnosis: a key milestone is reached and new questions are raised. Archives of pathology & laboratory medicine, 142(11), 1383-1387.

FDA approves stroke-detecting AI software. (2018). Nature Biotechnology, 36(4), 290. https://doi.org/10.1038/nbt0418-290

Fraser, K. C., Meltzer, J. A., & Rudzicz, F. (2015). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer S Disease, 49(2), 407–422. https://doi.org/10.3233/jad-150520

Galloway, C. D., Valys, A. V., Shreibati, J. B., Treiman, D. L., Petterson, F. L., Gundotra, V. P., Albert, D. E., Attia, Z. I., Carter, R. E., Asirvatham, S. J., Ackerman, M. J., Noseworthy, P. A., Dillon, J. J., & Friedman, P. A. (2019). Development and validation of a Deep-Learning model to screen for hyperkalemia from the electrocardiogram. JAMA Cardiology, 4(5), 428. https://doi.org/10.1001/jamacardio.2019.0640

Garrison, E., & Marth, G. (2012, July 17). Haplotype-based variant detection from short-read sequencing. arXiv.org. https://arxiv.org/abs/1207.3907

Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine, 178(11), 1544. https://doi.org/10.1001/jamainternmed.2018.3763

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Webster, D. R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. jama, 316(22), 2402-2410.

Gurovich, Y., Hanani, Y., Bar, O., Nadav, G., Fleischer, N., Gelbman, D., Basel-Salmon, L., Krawitz, P. M., Kamphausen, S. B., Zenker, M., Bird, L. M., & Gripp, K. W. (2018). Identifying facial phenotypes of genetic disorders using deep learning. Nature Medicine, 25(1), 60–64. https://doi.org/10.1038/s41591-018-0279-0

Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2018). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. https://doi.org/10.1038/s41591-018-0268-3

Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A. R., Jaitly, N., ... & Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal processing magazine, 29(6), 82-97.

Hsieh, T., Mensah, M. A., Pantel, J. T., Aguilar, D., Bar, O., Bayat, A., Becerra-Solano, L., Bentzen, H. B., Biskup, S., Borisov, O., Braaten, O., Ciaccio, C., Coutelier, M., Cremer, K., Danyel, M., Daschkey, S., Eden, H. D., Devriendt, K., Wilson, S., . . . Krawitz, P. M. (2019). PEDIA: prioritization of exome data by image analysis. Genetics in Medicine, 21(12), 2807–2814. https://doi.org/10.1038/s41436-019-0566-2

Hwang, S., Kim, E., Lee, I., & Marcotte, E. M. (2015). Systematic comparison of variant calling pipelines using gold standard personal exome variants. Scientific Reports, 5(1). https://doi.org/10.1038/srep17875

Inouye, M., Abraham, G., Nelson, C. P., Wood, A. M., Sweeting, M. J., Dudbridge, F., Lai, F. Y., Kaptoge, S., Brozynska, M., Wang, T., Ye, S., Webb, T. R., Rutter, M. K., Tzoulaki, I., Patel, R. S., Loos, R. J., Keavney, B., Hemingway, H., Thompson, J., . . . Samani, N. J. (2018). Genomic risk prediction of coronary artery disease in 480,000 adults. Journal of the American College of Cardiology, 72(16), 1883–1893. https://doi.org/10.1016/j.jacc.2018.07.079

Jaganathan, K., Panagiotopoulou, S. K., McRae, J. F., Darbandi, S. F., Knowles, D., Li, Y. I., ... & Farh, K. K. H. (2019). Predicting splicing from primary sequence with deep learning. Cell, 176(3), 535-548.

Kelley, D. R., Reshef, Y. A., Bileschi, M., Belanger, D., McLean, C. Y., & Snoek, J. (2018). Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Research, 28(5), 739–750. https://doi.org/10.1101/gr.227819.117

Kircher, M., Witten, D. M., Jain, P., O’Roak, B. J., Cooper, G. M., & Shendure, J. (2014). A general framework for estimating the relative pathogenicity of human genetic variants. Nature Genetics, 46(3), 310–315. https://doi.org/10.1038/ng.2892

Köhler, S., Carmody, L., Vasilevsky, N., Jacobsen, J. O. B., Danis, D., Gourdine, J., Gargano, M., Harris, N. L., Matentzoglu, N., McMurry, J. A., Osumi-Sutherland, D., Cipriani, V., Balhoff, J. P., Conlin, T., Blau, H., Baynam, G., Palmer, R., Gratian, D., Dawkins, H., . . . Robinson, P. N. (2018). Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources. Nucleic Acids Research, 47(D1), D1018–D1027. https://doi.org/10.1093/nar/gky1105

Kohut, K., Limb, S., & Crawford, G. (2019). The changing role of the genetic counsellor in the Genomics era. Current Genetic Medicine Reports, 7(2), 75–84. https://doi.org/10.1007/s40142-019-00163-w

Landrum, M. J., Lee, J. M., Benson, M., Brown, G. R., Chao, C., Chitipiralla, S., Gu, B., Hart, J., Hoffman, D., Jang, W., Karapetyan, K., Katz, K., Liu, C., Maddipatla, Z., Malheiro, A., McDaniel, K., Ovetsky, M., Riley, G., Zhou, G., . . . Maglott, D. R. (2017). ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Research, 46(D1), D1062–D1067. https://doi.org/10.1093/nar/gkx1153

Le, E., Wang, Y., Huang, Y., Hickman, S., & Gilbert, F. (2019). Artificial intelligence in breast imaging. Clinical Radiology, 74(5), 357–366. https://doi.org/10.1016/j.crad.2019.02.006

Lee, A., Mavaddat, N., Wilcox, A. N., Cunningham, A. P., Carver, T., Hartley, S., De Villiers, C. B., Izquierdo, A., Simard, J., Schmidt, M. K., Walter, F. M., Chatterjee, N., Garcia-Closas, M., Tischkowitz, M., Pharoah, P., Easton, D. F., & Antoniou, A. C. (2019). BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genetics in Medicine, 21(8), 1708–1718. https://doi.org/10.1038/s41436-018-0406-9

Lello, L., Avery, S. G., Tellier, L., Vazquez, A. I., De Los Campos, G., & Hsu, S. D. H. (2018). Accurate genomic prediction of human height. Genetics, 210(2), 477–497. https://doi.org/10.1534/genetics.118.301267

Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014). Deep learning of the tissue-regulated splicing code. Bioinformatics, 30(12), i121–i129. https://doi.org/10.1093/bioinformatics/btu277

Li, J., Deng, L., Haeb-Umbach, R., & Gong, Y. (2015). Robust automatic speech recognition: a bridge to practical applications.

Li, Z., Huang, J., & Hu, Z. (2019). Screening and diagnosis of chronic pharyngitis based on deep learning. International Journal of Environmental Research and Public Health, 16(10), 1688. https://doi.org/10.3390/ijerph16101688

Liang, H., Tsui, B. Y., Ni, H., Valentim, C. C. S., Baxter, S. L., Liu, G., Cai, W., Kermany, D. S., Sun, X., Chen, J., He, L., Zhu, J., Tian, P., Shao, H., Zheng, L., Hou, R., Hewett, S., Li, G., Liang, P., . . . Xia, H. (2019). Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nature Medicine, 25(3), 433–438. https://doi.org/10.1038/s41591-018-0335-9

Lumaka, A., Cosemans, N., Mampasi, A. L., Mubungu, G., Mvuama, N., Lubala, T., Mbuyi-Musanzayi, S., Breckpot, J., Holvoet, M., De Ravel, T., Van Buggenhout, G., Peeters, H., Donnai, D., Mutesa, L., Verloes, A., Tshilobo, P. L., & Devriendt, K. (2016). Facial dysmorphism is influenced by ethnic background of the patient and of the evaluator. Clinical Genetics, 92(2), 166–171. https://doi.org/10.1111/cge.12948

Maor, E., Sara, J. D., Orbelo, D. M., Lerman, L. O., Levanon, Y., & Lerman, A. (2018). Voice signal characteristics are independently associated with coronary artery disease. Mayo Clinic Proceedings, 93(7), 840–847. https://doi.org/10.1016/j.mayocp.2017.12.025

Marmar, C. R., Brown, A. D., Qian, M., Laska, E., Siegel, C., Li, M., Abu-Amara, D., Tsiartas, A., Richey, C., Smith, J., Knoth, B., & Vergyri, D. (2019). Speech-based markers for posttraumatic stress disorder in US veterans. Depression and Anxiety, 36(7), 607–616. https://doi.org/10.1002/da.22890

Martin, A. R., Kanai, M., Kamatani, Y., Okada, Y., Neale, B. M., & Daly, M. J. (2019). Clinical use of current polygenic risk scores may exacerbate health disparities. Nature Genetics, 51(4), 584–591. https://doi.org/10.1038/s41588-019-0379-x

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Scientific reports, 6(1), 1-10.

Mittelstadt, B., Russell, C., & Wachter, S. (2019, January). Explaining explanations in AI. In Proceedings of the conference on fairness, accountability, and transparency (pp. 279-288).

Mobadersany, P., Yousefi, S., Amgad, M., Gutman, D. A., Barnholtz-Sloan, J. S., Vega, J. E. V., Brat, D. J., & Cooper, L. a. D. (2018). Predicting cancer outcomes from histology and genomics using convolutional networks. Proceedings of the National Academy of Sciences, 115(13). https://doi.org/10.1073/pnas.1717139115

Mohr, D. N., Turner, D. W., Pond, G. R., Kamath, J. S., De Vos, C. B., & Carpenter, P. C. (2003). Speech recognition as a transcription aid: A randomized comparison with standard transcription. Journal of the American Medical Informatics Association, 10(1), 85–93. https://doi.org/10.1197/jamia.m1130

Niazi, M. K. K., Parwani, A. V., & Gurcan, M. N. (2019). Digital pathology and artificial intelligence. The lancet oncology, 20(5), e253-e261.

Olah, C., Mordvintsev, A., & Schubert, L. (2017). Feature visualization. Distill, 2(11). https://doi.org/10.23915/distill.00007

Parthasarathy, S., Rozgic, V., Sun, M., & Wang, C. (2019, May). Improving emotion classification through variational inference of latent variables. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 7410-7414). IEEE.

Poplin, R., Chang, P., Alexander, D., Schwartz, S., Colthurst, T., Ku, A., Newburger, D., Dijamco, J., Nguyen, N., Afshar, P. T., Gross, S. S., Dorfman, L., McLean, C. Y., & DePristo, M. A. (2018). A universal SNP and small-indel variant caller using deep neural networks. Nature Biotechnology, 36(10), 983–987. https://doi.org/10.1038/nbt.4235

Poplin, R., Varadarajan, A. V., Blumer, K., Liu, Y., McConnell, M. V., Corrado, G. S., Peng, L., & Webster, D. R. (2018). Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering, 2(3), 158–164. https://doi.org/10.1038/s41551-018-0195-0

Prabhavalkar, R., Rao, K., Sainath, T. N., Li, B., Johnson, L., & Jaitly, N. (2017, August). A Comparison of sequence-to-sequence models for speech recognition. In Interspeech (pp. 939-943).

Quang, D., & Xie, X. (2016). DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Research, 44(11), e107. https://doi.org/10.1093/nar/gkw226

Quang, D., Chen, Y., & Xie, X. (2014). DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics, 31(5), 761–763. https://doi.org/10.1093/bioinformatics/btu703

Rajkomar, A., Oren, E., Chen, K., Dai, A. M., Hajaj, N., Hardt, M., Liu, P. J., Liu, X., Marcus, J., Sun, M., Sundberg, P., Yee, H., Zhang, K., Zhang, Y., Flores, G., Duggan, G. E., Irvine, J., Le, Q., Litsch, K., . . . Dean, J. (2018). Scalable and accurate deep learning with electronic health records. Npj Digital Medicine, 1(1). https://doi.org/10.1038/s41746-018-0029-1

Retson, T. A., Besser, A. H., Sall, S., Golden, D., & Hsiao, A. (2019). Machine learning and deep neural networks in thoracic and cardiovascular imaging. Journal of Thoracic Imaging, 34(3), 192–201. https://doi.org/10.1097/rti.0000000000000385

Riesselman, A. J., Ingraham, J. B., & Marks, D. S. (2018). Deep generative models of genetic variation capture the effects of mutations. Nature Methods, 15(10), 816–822. https://doi.org/10.1038/s41592-018-0138-4

Ringeval, F., Schuller, B., Valstar, M., Cummins, N., Cowie, R., Tavabi, L., ... & Pantic, M. (2019, October). AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition. In Proceedings of the 9th International on Audio/visual Emotion Challenge and Workshop (pp. 3-12). https://doi.org/10.1145/3347320.3357688

Sankar, P. L., & Parker, L. S. (2016). The Precision Medicine Initiative’s All of Us Research Program: an agenda for research on its ethical, legal, and social issues. Genetics in Medicine, 19(7), 743–750. https://doi.org/10.1038/gim.2016.183

Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual explanations from deep networks via Gradient-Based Localization. https://openaccess.thecvf.com/content_iccv_2017/html/Selvaraju_Grad-CAM_Visual_Explanations_ICCV_2017_paper.html

Sirugo, G., Williams, S. M., & Tishkoff, S. A. (2019). The missing diversity in human genetic studies. Cell, 177(1), 26-31.

Soemedi, R., Cygan, K. J., Rhine, C. L., Wang, J., Bulacan, C., Yang, J., Bayrak-Toydemir, P., McDonald, J., & Fairbrother, W. G. (2017). Pathogenic variants that alter protein code often disrupt splicing. Nature Genetics, 49(6), 848–855. https://doi.org/10.1038/ng.3837

Sohail, M., Maier, R. M., Ganna, A., Bloemendal, A., Martin, A. R., Turchin, M. C., Chiang, C. W., Hirschhorn, J., Daly, M. J., Patterson, N., Neale, B., Mathieson, I., Reich, D., & Sunyaev, S. R. (2019). Polygenic adaptation on height is overestimated due to uncorrected stratification in genome-wide association studies. eLife, 8. https://doi.org/10.7554/elife.39702

Sudlow, C., Gallacher, J., Allen, N., Beral, V., Burton, P., Danesh, J., Downey, P., Elliott, P., Green, J., Landray, M., Liu, B., Matthews, P., Ong, G., Pell, J., Silman, A., Young, A., Sprosen, T., Peakman, T., & Collins, R. (2015). UK Biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Medicine, 12(3), e1001779. https://doi.org/10.1371/journal.pmed.1001779

Sundaram, L., Gao, H., Padigepati, S. R., McRae, J. F., Li, Y., Kosmicki, J. A., Fritzilas, N., Hakenberg, J., Dutta, A., Shon, J., Xu, J., Batzoglou, S., Li, X., & Farh, K. K. (2018). Predicting the clinical impact of human mutation with deep neural networks. Nature Genetics, 50(8), 1161–1170. https://doi.org/10.1038/s41588-018-0167-z

Tang, H., & Thomas, P. D. (2016). Tools for predicting the functional impact of nonsynonymous genetic variation. Genetics, 203(2), 635–647. https://doi.org/10.1534/genetics.116.190033

Telenti, A., Pierce, L. C. T., Biggs, W. H., Di Iulio, J., Wong, E. H. M., Fabani, M. M., Kirkness, E. F., Moustafa, A., Shah, N., Xie, C., Brewerton, S. C., Bulsara, N., Garner, C., Metzker, G., Sandoval, E., Perkins, B. A., Och, F. J., Turpaz, Y., & Venter, J. C. (2016). Deep sequencing of 10,000 human genomes. Proceedings of the National Academy of Sciences, 113(42), 11901–11906. https://doi.org/10.1073/pnas.1613365113

Tison, G. H., Sanchez, J. M., Ballinger, B., Singh, A., Olgin, J. E., Pletcher, M. J., Vittinghoff, E., Lee, E. S., Fan, S. M., Gladstone, R. A., Mikell, C., Sohoni, N., Hsieh, J., & Marcus, G. M. (2018). Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiology, 3(5), 409. https://doi.org/10.1001/jamacardio.2018.0136

Topol, E. J. (2018). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7

Torkamani, A., Andersen, K. G., Steinhubl, S. R., & Topol, E. J. (2017). High-Definition Medicine. Cell, 170(5), 828–843. https://doi.org/10.1016/j.cell.2017.08.007

Torkamani, A., Andersen, K. G., Steinhubl, S. R., & Topol, E. J. (2017). High-definition medicine. Cell, 170(5), 828-843.

Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018). The personal and clinical utility of polygenic risk scores. Nature Reviews Genetics, 19(9), 581–590. https://doi.org/10.1038/s41576-018-0018-x

Trigeorgis, G., Ringeval, F., Brueckner, R., Marchi, E., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2016, March). Adieu features? end-to-end speech emotion recognition using a deep convolutional recurrent network. In 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 5200-5204). IEEE.

Van Der Heijden, A. A., Abramoff, M. D., Verbraak, F., Van Hecke, M. V., Liem, A., & Nijpels, G. (2017). Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmologica, 96(1), 63–68. https://doi.org/10.1111/aos.13613

Vayena, E., Blasimme, A., & Cohen, I. G. (2018). Machine learning in medicine: Addressing ethical challenges. PLoS Medicine, 15(11), e1002689. https://doi.org/10.1371/journal.pmed.1002689

Velazquez, E. R., Parmar, C., Liu, Y., Coroller, T. P., Cruz, G., Stringfield, O., Ye, Z., Makrigiorgos, M., Fennessy, F., Mak, R. H., Gillies, R., Quackenbush, J., & Aerts, H. J. (2017). Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Research, 77(14), 3922–3930. https://doi.org/10.1158/0008-5472.can-17-0122

Wang, J., Cao, H., Zhang, J. Z. H., & Qi, Y. (2018). Computational Protein Design with Deep Learning Neural Networks. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-24760-x

Wick, R. R., Judd, L. M., & Holt, K. E. (2019). Performance of neural network basecalling tools for Oxford Nanopore sequencing. Genome Biology, 20(1). https://doi.org/10.1186/s13059-019-1727-y

Wu, Y. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144.

Yarnell, C. J., Fu, L., Manuel, D., Tanuseputro, P., Stukel, T., Pinto, R., Scales, D. C., Laupacis, A., & Fowler, R. A. (2017). Association between Immigrant Status and End-of-Life Care in Ontario, Canada. JAMA, 318(15), 1479. https://doi.org/10.1001/jama.2017.14418

Zhou, J., & Troyanskaya, O. G. (2015). Predicting effects of noncoding variants with deep learning–based sequence model. Nature Methods, 12(10), 931–934. https://doi.org/10.1038/nmeth.3547

Zhou, J., Park, C. Y., Theesfeld, C. L., Wong, A. K., Yuan, Y., Scheckel, C., Fak, J. J., Funk, J., Yao, K., Tajima, Y., Packer, A., Darnell, R. B., & Troyanskaya, O. G. (2019). Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk. Nature Genetics, 51(6), 973–980. https://doi.org/10.1038/s41588-019-0420-0

Zhou, J., Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., & Troyanskaya, O. G. (2018). Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nature Genetics, 50(8), 1171–1179. https://doi.org/10.1038/s41588-018-0160-6

Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., & Telenti, A. (2018). A primer on deep learning in genomics. Nature Genetics, 51(1), 12–18. https://doi.org/10.1038/s41588-018-0295-5