Deep learning in biomedicine

Abstract

Deep learning is beginning to impact biological research and biomedical applications as a result of its ability to integrate vast datasets, learn arbitrarily complex relationships and incorporate existing knowledge. Already, deep learning models can predict, with varying degrees of success, how genetic variation alters cellular processes involved in pathogenesis, which small molecules will modulate the activity of therapeutically relevant proteins, and whether radiographic images are indicative of disease. However, the flexibility of deep learning creates new challenges in guaranteeing the performance of deployed systems and in establishing trust with stakeholders, clinicians and regulators, who require a rationale for decision making. We argue that these challenges will be overcome using the same flexibility that created them; for example, by training deep models so that they can output a rationale for their predictions. Significant research in this direction will be needed to realize the full potential of deep learning in biomedicine.

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Figure 1: The rise of molecular biology, deep learning and data-driven biomedicine.
Figure 2: Supervised machine learning.
Figure 3: Key concepts in deep learning.

References

  1. 1

    Waldrop, M.M. Autonomous vehicles: no drivers required. Nature 518, 20–23 (2015).

    CAS  PubMed  Article  Google Scholar 

  2. 2

    Mnih, V. et al. Human-level control through deep reinforcement learning. Nature 518, 529–533 (2015).

    CAS  PubMed  Article  Google Scholar 

  3. 3

    Silver, D. et al. Mastering the game of Go without human knowledge. Nature 550, 354–359 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4

    Gatys, L.A., Ecker, A.S. & Bethge, M. Image style transfer using convolutional neural networks. in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2016.265 (2016).

  5. 5

    Graves, A., Mohamed, A.-R. & Hinton, G. Speech recognition with deep recurrent neural networks. in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing https://doi.org/10.1109/icassp.2013.6638947 (2013).

  6. 6

    Sutskever, I., Vinyals, O. & Le, Q.V. Sequence to sequence learning with neural networks. in. Neural Information Processing Systems 2014, 3104–3112 (2014).

    Google Scholar 

  7. 7

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8

    Angermueller, C., Pärnamaa, T., Parts, L. & Stegle, O. Deep learning for computational biology. Mol. Syst. Biol. 12, 878 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  9. 9

    Leung, M.K.K., Andrew, D., Babak, A. & Frey, B.J. Machine learning in genomic medicine: a review of computational problems and data sets. Proc. IEEE 104, 176–197 (2016).

    Article  Google Scholar 

  10. 10

    Mamoshina, P., Vieira, A., Putin, E. & Zhavoronkov, A. Applications of deep learning in biomedicine. Mol. Pharm. 13, 1445–1454 (2016).

    CAS  PubMed  Article  Google Scholar 

  11. 11

    Min, S., Lee, B. & Yoon, S. Deep learning in bioinformatics. Brief. Bioinform. 18, 851–869 (2017).

    PubMed  Google Scholar 

  12. 12

    Gawehn, E., Hiss, J.A. & Schneider, G. Deep learning in drug discovery. Mol. Inform. 35, 3–14 (2016).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  13. 13

    Jurtz, V.I. et al. An introduction to deep learning on biological sequence data: examples and solutions. Bioinformatics 33, 3685–3690 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14

    Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  15. 15

    Baldi, P. Deep learning in biomedical data science. Annu. Rev. Biomed. Data Sci. 1, 181–205 (2018).

    Article  Google Scholar 

  16. 16

    Ruder, S. An overview of multi-task learning in deep neural networks. Preprint at https://doi.org/arxiv.org/abs/1706.05098 (2017).

  17. 17

    Liu, H., Simonyan, K., Vinyals, O., Fernando, C. & Kavukcuoglu, K. Hierarchical representations for efficient architecture search. Preprint at https://doi.org/arxiv.org/abs/1711.00436 (2017).

  18. 18

    Weiss, K., Khoshgoftaar, T.M. & Wang, D. A survey of transfer learning. J. Big Data 3, 9 (2016).

    Article  Google Scholar 

  19. 19

    Krizhevsky, A., Sutskever, I. & Hinton, G.E. ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017).

    Article  Google Scholar 

  20. 20

    Schuster, M. & Paliwal, K.K. Bidirectional recurrent neural networks. IEEE Trans. Signal Process. 45, 2673–2681 (1997).

    Article  Google Scholar 

  21. 21

    Hinton, G.E., Dayan, P., Frey, B.J. & Neal, R.M. The wake-sleep algorithm for unsupervised neural networks. Science 268, 7761831 (1995).

    Article  Google Scholar 

  22. 22

    Goodfellow, I.J. et al. Generative adversarial networks. Preprint at https://doi.org/arxiv.org/abs/1406.2661 (2014).

  23. 23

    Tan, J., Ung, M., Cheng, C. & Greene, C.S. Unsupervised feature construction and knowledge extraction from genome-wide assays of breast cancer with denoising autoencoders. Pac. Symp. Biocomput. 2015, 132–143 (2015).

    Google Scholar 

  24. 24

    Gómez-Bombarelli, R. et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent. Sci. 4, 268–276 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  25. 25

    Kingma, D.P., Rezende, D.J., Mohamed, S. & Welling, M. Semi-supervised learning with deep generative models. Preprint at https://doi.org/arxiv.org/abs/1406.5298 (2014).

  26. 26

    Pham, H., Guan, M.Y., Zoph, B., Le, Q.V. & Dean, J. Efficient neural architecture search via parameter sharing. Preprint at https://doi.org/arxiv.org/abs/1802.03268 (2018).

  27. 27

    MacKay, D.J.C. A practical Bayesian framework for backpropagation networks. Neural Comput. 4, 448–472 (1992).

    Article  Google Scholar 

  28. 28

    Neal, R.M. Bayesian Learning for Neural Networks (Springer, Berlin and Heidelberg, Germany, 1996).

  29. 29

    Gal, Y. & Ghahramani, Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. Preprint at https://doi.org/arxiv.org/abs/1506.02142 (2015).

  30. 30

    Efron, B. Bootstrap methods: another look at the jackknife. Ann. Stat. 7, 1–26 (1979).

    Article  Google Scholar 

  31. 31

    Xiong, H.Y. et al. RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science 347, 1254806 (2015).

    Article  CAS  Google Scholar 

  32. 32

    Lipton, Z.C. The mythos of model interpretability. Preprint at https://doi.org/arxiv.org/abs/1606.03490 (2016).

  33. 33

    Pearl, J. Causal inference in statistics: an overview. Stat. Surv. 3, 96–146 (2009).

    Article  Google Scholar 

  34. 34

    Shrikumar, A., Greenside, P., Shcherbina, A. & Kundaje, A. Not just a black box: learning important features through propagating activation differences. Preprint at https://doi.org/arxiv.org/abs/1605.01713 (2016).

  35. 35

    Hoskins, R.A. et al. Reports from CAGI: the critical assessment of genome interpretation. Hum. Mutat. 38, 1039–1041 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  36. 36

    Visscher, P.M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37

    Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  38. 38

    Timpson, N.J., Greenwood, C.M.T., Soranzo, N., Lawson, D.J. & Richards, J.B. Genetic architecture: the shape of the genetic contribution to human traits and disease. Nat. Rev. Genet. 19, 110–124 (2018).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  39. 39

    Boyle, E.A., Li, Y.I. & Pritchard, J.K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40

    Wagih, O., Merico, D., Delong, A. & Frey, B.J. Allele-specific transcription factor binding as a benchmark for assessing variant impact predictors. Preprint at bioRxiv https://doi.org/10.1101/253427 (2018).

  41. 41

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

    CAS  Google Scholar 

  42. 42

    Kelley, D.R., Snoek, J. & Rinn, J.L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 26, 990–999 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43

    Zhou, J. & Troyanskaya, O.G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44

    Quang, D. & Xie, X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 44, e107 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  45. 45

    Angermueller, C., Lee, H.J., Reik, W. & Stegle, O. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol. 18, 67 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  46. 46

    Zhang, S., Hu, H., Jiang, T., Zhang, L. & Zeng, J. TITER: predicting translation initiation sites by deep learning. Bioinformatics 33, i234–i242 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47

    Shendure, J. & Fields, S. Massively parallel genetics. Genetics 203, 617–619 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48

    Ma, J. et al. Using deep learning to model the hierarchical structure and function of a cell. Nat. Methods 15, 290–298 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49

    Baldi, P., Brunak, S., Frasconi, P., Soda, G. & Pollastri, G. Exploiting the past and the future in protein secondary structure prediction. Bioinformatics 15, 937–946 (1999).

    CAS  PubMed  Article  Google Scholar 

  50. 50

    Pollastri, G., Przybylski, D., Rost, B. & Baldi, P. Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles. Proteins 47, 228–235 (2002).

    CAS  PubMed  Article  Google Scholar 

  51. 51

    Duvenaud, D. et al. Convolutional networks on graphs for learning molecular fingerprints. Preprint at https://doi.org/arxiv.org/abs/1509.09292 (2015).

  52. 52

    Kearnes, S., McCloskey, K., Berndl, M., Pande, V. & Riley, P. Molecular graph convolutions: moving beyond fingerprints. J. Comput. Aided Mol. Des. 30, 595–608 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53

    Dahl, G.E., Jaitly, N. & Salakhutdinov, R. Multi-task neural networks for QSAR predictions. Preprint at https://doi.org/arxiv.org/abs/1406.1231 (2014).

  54. 54

    Ma, J., Sheridan, R.P., Liaw, A., Dahl, G.E. & Svetnik, V. Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model. 55, 263–274 (2015).

    CAS  PubMed  Article  Google Scholar 

  55. 55

    Ramsundar, B. et al. Massively multitask networks for drug discovery. Preprint at https://doi.org/arxiv.org/abs/1502.02072 (2015).

  56. 56

    Wallach, I., Dzamba, M. & Heifets, A. AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. Preprint at https://doi.org/arxiv.org/abs/1510.02855 (2015).

  57. 57

    Liu, Y. et al. Detecting cancer metastases on gigapixel pathology images. Preprint at https://doi.org/arxiv.org/abs/1703.02442 (2017).

  58. 58

    Wang, D., Khosla, A., Gargeya, R., Irshad, H. & Beck, A.H. Deep learning for identifying metastatic breast cancer. Preprint at https://doi.org/arxiv.org/abs/1606.05718 (2016).

  59. 59

    Lakhani, P. & Sundaram, B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284, 574–582 (2017).

    PubMed  Article  PubMed Central  Google Scholar 

  60. 60

    Kraus, O.Z. et al. Automated analysis of high-content microscopy data with deep learning. Mol. Syst. Biol. 13, 924 (2017).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  61. 61

    Carpenter, A.E. et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7, R100 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  62. 62

    Bruno, M.A., Walker, E.A. & Abujudeh, H.H. Understanding and confronting our mistakes: the epidemiology of error in radiology and strategies for error reduction. Radiographics 35, 1668–1676 (2015).

    PubMed  Article  Google Scholar 

  63. 63

    Leinonen, R., Sugawara, H. & Shumway, M. The Sequence Read Archive. Nucleic Acids Res. 39, D19–D21 (2011).

    CAS  PubMed  Article  Google Scholar 

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Acknowledgements

Our perspectives were influenced by conversations with many people, including members of Deep Genomics, B. Andrews, Y. Bengio, B. Blencowe, C. Boone, D. Botstein, C. Francis, A. Heifets, G. Hinton, T. Hughes, P. Hutt, R. Klausner, E. Lander, Y. LeCun, A. Levin, Q. Morris, B. Neale, S. Scherer and J.C. Venter.

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Correspondence to Brendan J Frey.

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All authors are, or recently were, employees of Deep Genomics, an AI therapeutics company, which is using deep learning to identify the genetic determinants of disease and to develop therapies.

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Wainberg, M., Merico, D., Delong, A. et al. Deep learning in biomedicine. Nat Biotechnol 36, 829–838 (2018). https://doi.org/10.1038/nbt.4233

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