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Deep learning: new computational modelling techniques for genomics

Abstract

As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.

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Fig. 1: Neural networks with hidden layers used to model nonlinear dependencies.
Fig. 2: Modelling transcription factor binding sites and spacing with convolutional neural networks.
Fig. 3: Neural network layers and their parameter-sharing schemes.
Fig. 4: Multitask models, multimodal models and transfer learning.
Fig. 5: Model interpretation via feature importance scores.
Fig. 6: Unsupervised learning.

References

  1. 1.

    Hieter, P. & Boguski, M. Functional genomics: it’s all how you read it. Science 278, 601–602 (1997).

    CAS  Google Scholar 

  2. 2.

    Brown, P. O. & Botstein, D. Exploring the new world of the genome with DNA microarrays. Nat. Genet. 21, 33–37 (1999).

    CAS  Google Scholar 

  3. 3.

    Ozaki, K. et al. Functional SNPs in the lymphotoxin-α gene that are associated with susceptibility to myocardial infarction. Nat. Genet. 32, 650–654 (2002).

    CAS  Google Scholar 

  4. 4.

    Golub, T. R. et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286, 531–537 (1999).

    CAS  Google Scholar 

  5. 5.

    Oliver, S. Guilt-by-association goes global. Nature 403, 601–603 (2000).

    CAS  Google Scholar 

  6. 6.

    The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    PubMed Central  PubMed  Google Scholar 

  7. 7.

    Murphy, K. P. Machine Learning: A Probabilistic Perspective (MIT Press, 2012).

  8. 8.

    Bishop, C. M. Pattern Recognition and Machine Learning (Springer, New York, 2016).

  9. 9.

    Libbrecht, M. W. & Noble, W. S. Machine learning applications in genetics and genomics. Nat. Rev. Genet. 16, 321–332 (2015).

    CAS  PubMed Central  PubMed  Google Scholar 

  10. 10.

    Durbin, R., Eddy, S. R., Krogh, A. & Mitchison, G. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids (Cambridge Univ. Press, 1998).

  11. 11.

    Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016). This textbook covers theoretical and practical aspects of deep learning with introductory sections on linear algebra and machine learning.

  12. 12.

    Shi, S., Wang, Q., Xu, P. & Chu, X. in 2016 7th International Conference on Cloud Computing and Big Data (CCBD) 99–104 (IEEE, 2016).

  13. 13.

    Krizhevsky, A., Sutskever, I. & Hinton, G. E. in Advances in Neural Information Processing Systems 25 (NIPS 2012) (eds Pereira, F., Burges, C. J. C., Bottou, L. & Weinberger, K. Q.) 1097–1105 (Curran Associates, Inc., 2012).

  14. 14.

    Girshick, R., Donahue, J., Darrell, T. & Malik, J. in 2014 IEEE Conference on Computer Vision and Pattern Recognition 580–587 (IEEE, 2014).

  15. 15.

    Long, J., Shelhamer, E. & Darrell, T. in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 3431–3440 (IEEE, 2015).

  16. 16.

    Hannun, A. et al. Deep speech: scaling up end-to-end speech recognition. Preprint at arXiv https://arxiv.org/abs/1412.5567 (2014).

  17. 17.

    Wu, Y. et al. Google’s neural machine translation system: bridging the gap between human and machine translation. Preprint at arXiv https://arxiv.org/abs/1609.08144 (2016).

  18. 18.

    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). This paper describes a pioneering convolutional neural network application in genomics.

    CAS  Google Scholar 

  19. 19.

    Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015). This paper applies deep CNNs to predict chromatin features and transcription factor binding from DNA sequence and demonstrates its utility in non-coding variant effect prediction.

    CAS  PubMed Central  PubMed  Google Scholar 

  20. 20.

    Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51, 12–18 (2019).

    CAS  Google Scholar 

  21. 21.

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

    PubMed Central  PubMed  Google Scholar 

  22. 22.

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

    Google Scholar 

  23. 23.

    Jones, W., Alasoo, K., Fishman, D. & Parts, L. Computational biology: deep learning. Emerg. Top. Life Sci. 1, 257–274 (2017).

    Google Scholar 

  24. 24.

    Wainberg, M., Merico, D., Delong, A. & Frey, B. J. Deep learning in biomedicine. Nat. Biotechnol. 36, 829–838 (2018).

    CAS  Google Scholar 

  25. 25.

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

    PubMed Central  PubMed  Google Scholar 

  26. 26.

    Morgan, J. N. & Sonquist, J. A. Problems in the analysis of survey data, and a proposal. J. Am. Stat. Assoc. 58, 415–434 (1963).

    Google Scholar 

  27. 27.

    Boser, B. E., Guyon, I. M. & Vapnik, V. N. A. in Proceedings of the Fifth Annual Workshop on Computational Learning Theory 144–152 (ACM, 1992).

  28. 28.

    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).

    Google Scholar 

  29. 29.

    Friedman, J. H. Greedy function approximation: a gradient boosting machine. Ann. Stat. 29, 1189–1232 (2001).

    Google Scholar 

  30. 30.

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

    Google Scholar 

  31. 31.

    Jha, A., Gazzara, M. R. & Barash, Y. Integrative deep models for alternative splicing. Bioinformatics 33, i274–i282 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  32. 32.

    Quang, D., Chen, Y. & Xie, X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31, 761–763 (2015).

    CAS  Google Scholar 

  33. 33.

    Liu, F., Li, H., Ren, C., Bo, X. & Shu, W. PEDLA: predicting enhancers with a deep learning-based algorithmic framework. Sci. Rep. 6, 28517 (2016).

    CAS  PubMed Central  PubMed  Google Scholar 

  34. 34.

    Li, Y., Shi, W. & Wasserman, W. W. Genome-wide prediction of cis-regulatory regions using supervised deep learning methods. BMC Bioinformatics 19, 202 (2018).

    PubMed Central  PubMed  Google Scholar 

  35. 35.

    Johnson, D. S., Mortazavi, A., Myers, R. M. & Wold, B. Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

    CAS  Google Scholar 

  36. 36.

    Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

    CAS  PubMed Central  PubMed  Google Scholar 

  37. 37.

    Robertson, G. et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651–657 (2007).

    CAS  Google Scholar 

  38. 38.

    Park, P. J. ChIP-seq: advantages and challenges of a maturing technology. Nat. Rev. Genet. 10, 669–680 (2009).

    CAS  PubMed Central  PubMed  Google Scholar 

  39. 39.

    Weirauch, M. T. et al. Evaluation of methods for modeling transcription factor sequence specificity. Nat. Biotechnol. 31, 126 (2013).

    CAS  PubMed Central  PubMed  Google Scholar 

  40. 40.

    Lee, D., Karchin, R. & Beer, M. A. Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 21, 2167–2180 (2011).

    CAS  PubMed Central  PubMed  Google Scholar 

  41. 41.

    Ghandi, M., Lee, D., Mohammad-Noori, M. & Beer, M. A. Enhanced regulatory sequence prediction using gapped k-mer features. PLOS Comput. Biol. 10, e1003711 (2014).

    PubMed Central  PubMed  Google Scholar 

  42. 42.

    Stormo, G. D., Schneider, T. D., Gold, L. & Ehrenfeucht, A. Use of the ‘Perceptron’ algorithm to distinguish translational initiation sites in E. coli. Nucleic Acids Res. 10, 2997–3011 (1982).

    CAS  PubMed Central  PubMed  Google Scholar 

  43. 43.

    Stormo, G. D. DNA binding sites: representation and discovery. Bioinformatics 16, 16–23 (2000).

    CAS  Google Scholar 

  44. 44.

    D’haeseleer, P. What are DNA sequence motifs? Nat. Biotechnol. 24, 423–425 (2006).

    Google Scholar 

  45. 45.

    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). This paper describes the application of a deep CNN to predict chromatin accessibility in 164 cell types from DNA sequence.

    CAS  PubMed Central  PubMed  Google Scholar 

  46. 46.

    Wang, M., Tai, C., E, W. & Wei, L. DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants. Nucleic Acids Res. 46, e69 (2018).

    PubMed Central  PubMed  Google Scholar 

  47. 47.

    Kelley, D. R. et al. Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Res. 28, 739–750 (2018). In this paper, a deep CNN was trained to predict more than 4,000 genomic measurements including gene expression as measured by cap analysis of gene expression (CAGE) for every 150 bp in the genome using a receptive field of 32 kb.

    CAS  PubMed Central  PubMed  Google Scholar 

  48. 48.

    Schreiber, J., Libbrecht, M., Bilmes, J. & Noble, W. Nucleotide sequence and DNaseI sensitivity are predictive of 3D chromatin architecture. Preprint at bioRxiv https://doi.org/10.1101/103614 (2018).

    Article  Google Scholar 

  49. 49.

    Zeng, H. & Gifford, D. K. Predicting the impact of non-coding variants on DNA methylation. Nucleic Acids Res. 45, e99 (2017).

    PubMed Central  PubMed  Google Scholar 

  50. 50.

    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 Central  PubMed  Google Scholar 

  51. 51.

    Zhou, J. et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet. 50, 1171–1179 (2018). In this paper, two models, a deep CNN and a linear model, are stacked to predict tissue-specific gene expression from DNA sequence, which demonstrates the utility of this approach in non-coding variant effect prediction.

    CAS  PubMed Central  PubMed  Google Scholar 

  52. 52.

    Cuperus, J. T. et al. Deep learning of the regulatory grammar of yeast 5’ untranslated regions from 500,000 random sequences. Genome Res. 27, 2015–2024 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  53. 53.

    Pan, X. & Shen, H.-B. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinformatics 18, 136 (2017).

    PubMed Central  PubMed  Google Scholar 

  54. 54.

    Avsec, Ž., Barekatain, M., Cheng, J. & Gagneur, J. Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks. Bioinformatics 34, 1261–1269 (2018).

    CAS  Google Scholar 

  55. 55.

    Budach, S. & Marsico, A. pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks. Bioinformatics 34, 3035–3037 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  56. 56.

    Cheng, S. et al. MiRTDL: a deep learning approach for miRNA target prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. 13, 1161–1169 (2016).

    Google Scholar 

  57. 57.

    Kim, H. K. et al. Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity. Nat. Biotechnol. 36, 239–241 (2018).

    CAS  Google Scholar 

  58. 58.

    Koh, P. W., Pierson, E. & Kundaje, A. Denoising genome-wide histone ChIP-seq with convolutional neuralnetworks. Bioinformatics 33, i225–i233 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  59. 59.

    Zhang, Y. et al. Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus. Nat. Commun. 9, 750 (2018).

    PubMed Central  PubMed  Google Scholar 

  60. 60.

    Nielsen, A. A. K. & Voigt, C. A. Deep learning to predict the lab-of-origin of engineered DNA. Nat. Commun. 9, 3135 (2018).

    PubMed Central  PubMed  Google Scholar 

  61. 61.

    Luo, R., Sedlazeck, F. J., Lam, T.-W. & Schatz, M. Clairvoyante: a multi-task convolutional deep neural network for variant calling in single molecule sequencing. Preprint at bioRxiv https://doi.org/10.1101/310458 (2018).

    Article  Google Scholar 

  62. 62.

    Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983–987 (2018). In this paper, a deep CNN is trained to call genetic variants from different DNA-sequencing technologies.

    CAS  Google Scholar 

  63. 63.

    Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548 (2019).

    CAS  Google Scholar 

  64. 64.

    Elman, J. L. Finding structure in time. Cogn. Sci. 14, 179–211 (1990).

    Google Scholar 

  65. 65.

    Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).

    CAS  Google Scholar 

  66. 66.

    Bai, S., Zico Kolter, J. & Koltun, V. An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. Preprint at arXiv https://arxiv.org/abs/1803.01271 (2018).

  67. 67.

    Pan, X., Rijnbeek, P., Yan, J. & Shen, H.-B. Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genomics 19, 511 (2018).

    PubMed Central  PubMed  Google Scholar 

  68. 68.

    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 Central  PubMed  Google Scholar 

  69. 69.

    Quang, D. & Xie, X. FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data. Preprint at bioRxiv https://doi.org/10.1101/151274 (2017).

    Article  Google Scholar 

  70. 70.

    Lee, B., Baek, J., Park, S. & Yoon, S. in Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics 434–442 (ACM, 2016).

  71. 71.

    Park, S., Min, S., Choi, H. & Yoon, S. deepMiRGene: deep neural network based precursor microRNA prediction. Preprint at arXiv https://arxiv.org/abs/1605.00017 (2016).

  72. 72.

    Boža, V., Brejová, B. & Vinař;, T. DeepNano: deep recurrent neural networks for base calling in MinION nanopore reads. PLOS ONE 12, e0178751 (2017).

    PubMed Central  PubMed  Google Scholar 

  73. 73.

    Mikheyev, A. S. & Tin, M. M. Y. A first look at the Oxford Nanopore MinION sequencer. Mol. Ecol. Resour. 14, 1097–1102 (2014).

    CAS  Google Scholar 

  74. 74.

    Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).

    PubMed Central  PubMed  Google Scholar 

  75. 75.

    Mitra, K., Carvunis, A.-R., Ramesh, S. K. & Ideker, T. Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719–732 (2013).

    CAS  PubMed Central  PubMed  Google Scholar 

  76. 76.

    Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M. & Monfardini, G. The graph neural network model. IEEE Trans. Neural Netw. 20, 61–80 (2009).

    Google Scholar 

  77. 77.

    Defferrard, M., Bresson, X. & Vandergheynst, P. in Advances in Neural Information Processing Systems 29 (NIPS 2016) (eds Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, I. & Garnett, R.) 3844–3852 (Curran Associates Inc., 2016).

  78. 78.

    Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. Preprint at arXiv https://arxiv.org/abs/1609.02907 (2016).

  79. 79.

    Battaglia, P. W. et al. Relational inductive biases, deep learning, and graph networks. Preprint at arXiv https://arxiv.org/abs/1806.01261 (2018).

  80. 80.

    Hamilton, W. L., Ying, R. & Leskovec, J. Inductive representation learning on large graphs. Preprint at arXiv https://arxiv.org/abs/1706.02216 (2017).

  81. 81.

    Chen, J., Ma, T. & Xiao, C. FastGCN: fast learning with graph convolutional networks via importance sampling. Preprint at arXiv https://arxiv.org/abs/1801.10247 (2018).

  82. 82.

    Zitnik, M. & Leskovec, J. Predicting multicellular function through multi-layer tissue networks. Bioinformatics 33, i190–i198 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  83. 83.

    Zitnik, M., Agrawal, M. & Leskovec, J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics 34, i457–i466 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  84. 84.

    Duvenaud, D. K. et al. in Advances in Neural Information Processing Systems 28 (NIPS 2015) (eds Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M. & Garnett, R.) 2224–2232 (Curran Associates Inc., 2015).

  85. 85.

    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 Central  PubMed  Google Scholar 

  86. 86.

    Dutil, F., Cohen, J. P., Weiss, M., Derevyanko, G. & Bengio, Y. Towards gene expression convolutions using gene interaction graphs. Preprint at arXiv https://arxiv.org/abs/1806.06975 (2018).

  87. 87.

    Rhee, S., Seo, S. & Kim, S. in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence 3527–3534 (IJCAI, 2018).

  88. 88.

    Chen, Z., Badrinarayanan, V., Lee, C.-Y. & Rabinovich, A. GradNorm: gradient normalization for adaptive loss balancing in deep multitask networks. Preprint at arXiv https://arxiv.org/abs/1711.02257 (2017).

  89. 89.

    Sung, K. & Poggio, T. Example-based learning for view-based human face detection. IEEE Trans. Pattern Anal. Mach. Intell. 20, 39–51 (1998).

    Google Scholar 

  90. 90.

    Felzenszwalb, P. F., Girshick, R. B., McAllester, D. & Ramanan, D. Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010).

    Google Scholar 

  91. 91.

    Guo, M., Haque, A., Huang, D.-A., Yeung, S. & Fei-Fei, L. in Computer Vision – ECCV 2018 (eds Ferrari, V., Hebert, M., Sminchisescu, C. & Weiss, Y.) Vol. 11220 282–299 (Springer International Publishing, 2018).

  92. 92.

    Sundaram, L. et al. Predicting the clinical impact of human mutation with deep neural networks. Nat. Genet. 50, 1161–1170 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  93. 93.

    Zitnik, M. et al. Machine learning for integrating data in biology and medicine: principles, practice, and opportunities. Inf. Fusion 50, 71–91 (2018).

    Google Scholar 

  94. 94.

    Yosinski, J., Clune, J., Bengio, Y. & Lipson, H. in Advances in Neural Information Processing Systems 27 (NIPS 2014) (eds Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q.) 3320–3328 (Curran Associates Inc., 2014).

  95. 95.

    Kornblith, S., Shlens, J. & Le, Q. V. Do better ImageNet models transfer better? Preprint at arXiv https://arxiv.org/abs/1805.08974 (2018).

  96. 96.

    Russakovsky, O. et al. ImageNet large scale visual recognition challenge. Preprint at arXiv https://arxiv.org/abs/1409.0575 (2014).

  97. 97.

    Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    CAS  Google Scholar 

  98. 98.

    Pawlowski, N., Caicedo, J. C., Singh, S., Carpenter, A. E. & Storkey, A. Automating morphological profiling with generic deep convolutional networks. Preprint at bioRxiv https://doi.org/10.1101/085118 (2016).

    Article  Google Scholar 

  99. 99.

    Zeng, T., Li, R., Mukkamala, R., Ye, J. & Ji, S. Deep convolutional neural networks for annotating gene expression patterns in the mouse brain. BMC Bioinformatics 16, 147 (2015).

    PubMed Central  PubMed  Google Scholar 

  100. 100.

    Zhang, W. et al. in IEEE Transactions on Big Data (IEEE, 2018).

  101. 101.

    Adam, P. et al. Automatic differentiation in PyTorch. Presented at 31st Conference on Neural Information Processing Systems (NIPS 2017).

  102. 102.

    Abadi, M. et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems. Preprint at arXiv https://arxiv.org/abs/1603.04467 (2016).

  103. 103.

    Avsec, Z. et al. Kipoi: accelerating the community exchange and reuse of predictive models for genomics. Preprint at bioRxiv https://doi.org/10.1101/375345 (2018).This paper describes a platform to exchange trained predictive models in genomics including deep neural networks.

    Article  Google Scholar 

  104. 104.

    Breiman, L. Statistical modeling: the two cultures (with comments and a rejoinder by the author). Stat. Sci. 16, 199–231 (2001).

    Google Scholar 

  105. 105.

    Greenside, P., Shimko, T., Fordyce, P. & Kundaje, A. Discovering epistatic feature interactions from neural network models of regulatory DNA sequences. Bioinformatics 34, i629–i637 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  106. 106.

    Zeiler, M. D. & Fergus, R. in Computer Vision – ECCV 2014 (eds Fleet, D., Pajdla, T., Schiele, B. & Tuytelaars, T.) Vol. 8689 818–833 (Springer International Publishing, 2014).

  107. 107.

    Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. Preprint at arXiv https://arxiv.org/abs/1312.6034 (2013).

  108. 108.

    Shrikumar, A., Greenside, P., Shcherbina, A. & Kundaje, A. Not just a black box: learning important features through propagating activation differences. Preprint at arXiv https://arxiv.org/abs/1605.01713 (2016). This paper introduces DeepLIFT, a neural network interpretation method that highlights inputs most influential for the prediction.

  109. 109.

    Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. Preprint at arXiv https://arxiv.org/abs/1703.01365 (2017).

  110. 110.

    Lanchantin, J., Singh, R., Wang, B. & Qi, Y. Deep motif dashboard: visualizing and understanding genomic sequences using deep neural networks. Pac. Symp. Biocomput. 22, 254–265 (2017).

    PubMed Central  PubMed  Google Scholar 

  111. 111.

    Shrikumar, A. et al. TF-MoDISco v0.4.4.2-alpha: technical note. Preprint at arXiv https://arxiv.org/abs/1811.00416v2 (2018).

  112. 112.

    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 Central  PubMed  Google Scholar 

  113. 113.

    Hinton, G. E. & Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 313, 504–507 (2006).

    CAS  Google Scholar 

  114. 114.

    Kramer, M. A. Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 37, 233–243 (1991).

    CAS  Google Scholar 

  115. 115.

    Vincent, P., Larochelle, H., Bengio, Y. & Manzagol, P.-A. in Proceedings of the 25th International Conference on Machine Learning 1096–1103 (ACM, 2008).

  116. 116.

    Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y. & Manzagol, P.-A. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11, 3371–3408 (2010).

    Google Scholar 

  117. 117.

    Jolliffe, I. in International Encyclopedia of Statistical Science (ed. Lovric, M.) 1094–1096 (Springer Berlin Heidelberg, 2011).

  118. 118.

    Plaut, E. From principal subspaces to principal components with linear autoencoders. Preprint at arXiv https://arxiv.org/abs/1804.10253 (2018).

  119. 119.

    Kunin, D., Bloom, J. M., Goeva, A. & Seed, C. Loss landscapes of regularized linear autoencoders. Preprint at arXiv https://arxiv.org/abs/1901.08168 (2019).

  120. 120.

    Scholz, M., Kaplan, F., Guy, C. L., Kopka, J. & Selbig, J. Non-linear PCA: a missing data approach. Bioinformatics 21, 3887–3895 (2005).

    CAS  Google Scholar 

  121. 121.

    Tan, J., Hammond, J. H., Hogan, D. A. & Greene, C. S. ADAGE-based integration of publicly available Pseudomonas aeruginosa gene expression data with denoising autoencoders illuminates microbe-host interactions. mSystems 1, e00025–15 (2016).

    PubMed Central  PubMed  Google Scholar 

  122. 122.

    Tan, J. et al. ADAGE signature analysis: differential expression analysis with data-defined gene sets. BMC Bioinformatics 18, 512 (2017).

    PubMed Central  PubMed  Google Scholar 

  123. 123.

    Tan, J. et al. Unsupervised extraction of stable expression signatures from public compendia with an ensemble of neural networks. Cell Syst. 5, 63–71 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  124. 124.

    Brechtmann, F. et al. OUTRIDER: a statistical method for detecting aberrantly expressed genes in RNA sequencing data. Am. J. Hum. Genet. 103, 907–917 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  125. 125.

    Ding, J., Condon, A. & Shah, S. P. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat. Commun. 9, 2002 (2018).

    PubMed Central  PubMed  Google Scholar 

  126. 126.

    Cho, H., Berger, B. & Peng, J. Generalizable and scalable visualization of single-cell data using neural networks. Cell Syst. 7, 185–191 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  127. 127.

    Deng, Y., Bao, F., Dai, Q., Wu, L. & Altschuler, S. Massive single-cell RNA-seq analysis and imputation via deep learning. Preprint at bioRxiv https://doi.org/10.1101/315556 (2018).

    Article  Google Scholar 

  128. 128.

    Talwar, D., Mongia, A., Sengupta, D. & Majumdar, A. AutoImpute: autoencoder based imputation of single-cell RNA-seq data. Sci. Rep. 8, 16329 (2018).

    PubMed Central  PubMed  Google Scholar 

  129. 129.

    Amodio, M. et al. Exploring single-cell data with deep multitasking neural networks. Preprint at bioRxiv https://doi.org/10.1101/237065 (2019).

    Article  Google Scholar 

  130. 130.

    Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S. & Theis, F. J. Single-cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10, 390 (2019).

    PubMed Central  PubMed  Google Scholar 

  131. 131.

    Lin, C., Jain, S., Kim, H. & Bar-Joseph, Z. Using neural networks for reducing the dimensions of single-cell RNA-Seq data. Nucleic Acids Res. 45, e156 (2017).

    PubMed Central  PubMed  Google Scholar 

  132. 132.

    Kingma, D. P. & Welling, M. Auto-encoding variational bayes. Preprint at arXiv https://arxiv.org/abs/1312.6114 (2013).

  133. 133.

    Goodfellow, I. et al. in Advances in Neural Information Processing Systems 27 (NIPS 2014) (eds Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N. D. & Weinberger, K. Q.) 2672–2680 (Curran Associates Inc., 2014).

  134. 134.

    Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  135. 135.

    Way, G. P. & Greene, C. S. in Biocomputing 2018: Proceedings of the Pacific Symposium (eds Altman, R. B. et al.) 80–91 (World Scientific, 2018).

  136. 136.

    Grønbech, C. H. et al. scVAE: variational auto-encoders for single-cell gene expression data. Preprint at bioRxiv https://doi.org/10.1101/318295 (2018).

  137. 137.

    Wang, D. & Gu, J. VASC: dimension reduction and visualization of single-cell RNA-seq data by deep variational autoencoder. Genomics Proteomics Bioinformatics 16, 320–331 (2018).

    PubMed Central  PubMed  Google Scholar 

  138. 138.

    Lotfollahi, M., Alexander Wolf, F. & Theis, F. J. Generative modeling and latent space arithmetics predict single-cell perturbation response across cell types, studies and species. Preprint at bioRxiv https://doi.org/10.1101/478503 (2018).

    Article  Google Scholar 

  139. 139.

    Hu, Q. & Greene, C. S. Parameter tuning is a key part of dimensionality reduction via deep variational autoencoders for single cell RNA transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/385534 (2018).

    Article  Google Scholar 

  140. 140.

    Gupta, A. & Zou, J. Feedback GAN (FBGAN) for DNA: a novel feedback-loop architecture for optimizing protein functions. Preprint at arXiv https://arxiv.org/abs/1804.01694 (2018).

  141. 141.

    Killoran, N., Lee, L. J., Delong, A., Duvenaud, D. & Frey, B. J. Generating and designing DNA with deep generative models. Preprint at arXiv https://arxiv.org/abs/1712.06148 (2017).

  142. 142.

    Ghahramani, A., Watt, F. M. & Luscombe, N. M. Generative adversarial networks simulate gene expression and predict perturbations in single cells. Preprint at bioRxiv https://doi.org/10.1101/262501 (2018).

    Article  Google Scholar 

  143. 143.

    Amodio, M. & Krishnaswamy, S. MAGAN: aligning biological manifolds. Preprint at arXiv https://arxiv.org/abs/1803.00385 (2018).

  144. 144.

    Maurano, M. T. et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 337, 1190–1195 (2012).

    CAS  PubMed Central  PubMed  Google Scholar 

  145. 145.

    Cheng, J. et al. MMSplice: modular modeling improves the predictions of genetic variant effects on splicing. Genome Biol. 20, 48 (2019).

    PubMed Central  PubMed  Google Scholar 

  146. 146.

    van der Maaten, L. in Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (eds van Dyk, D. & Welling, M.) Vol. 5 384–391 (PMLR, 2009).

  147. 147.

    Angerer, P. et al. Single cells make big data: new challenges and opportunities in transcriptomics. Curr. Opin. Syst. Biol. 4, 85–91 (2017).

    Google Scholar 

  148. 148.

    Shaham, U. et al. Removal of batch effects using distribution-matching residual networks. Bioinformatics 33, 2539–2546 (2017).

    CAS  PubMed Central  PubMed  Google Scholar 

  149. 149.

    Regev, A. et al. The human cell atlas. eLife 6, e27041 (2017).

    PubMed Central  PubMed  Google Scholar 

  150. 150.

    Fleming, N. How artificial intelligence is changing drug discovery. Nature 557, S55–S57 (2018).

    CAS  Google Scholar 

  151. 151.

    Kalinin, A. A. et al. Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics 19, 629–650 (2018).

    CAS  PubMed Central  PubMed  Google Scholar 

  152. 152.

    AlQuraishi, M. End-to-end differentiable learning of protein structure. Preprint at bioRxiv https://doi.org/10.1101/265231 (2018).

    Article  Google Scholar 

  153. 153.

    Nawy, T. Spatial transcriptomics. Nat. Methods 15, 30 (2018).

    CAS  Google Scholar 

  154. 154.

    Eulenberg, P. et al. Reconstructing cell cycle and disease progression using deep learning. Nat. Commun. 8, 463 (2017).

    PubMed Central  PubMed  Google Scholar 

  155. 155.

    KoneČný, J., McMahan, H. B., Ramage, D. & Richtárik, P. Federated optimization: distributed machine learning for on-device intelligence. Preprint at arXiv https://arxiv.org/abs/1610.02527 (2016).

  156. 156.

    Beaulieu-Jones, B. K. et al. Privacy-preserving generative deep neural networks support clinical data sharing. Preprint at bioRxiv https://doi.org/10.1101/159756 (2018).

  157. 157.

    Lever, J., Krzywinski, M. & Altman, N. Classification evaluation. Nat. Methods 13, 603 (2016).

    CAS  Google Scholar 

  158. 158.

    Tieleman, T. & Hinton, G. Lecture 6.5 - RMSProp, COURSERA: neural networks for machine learning (2012).

  159. 159.

    Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at arXiv https://arxiv.org/abs/1412.6980 (2014).

  160. 160.

    Schmidhuber, J. Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015).

    PubMed  Google Scholar 

  161. 161.

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

    CAS  Google Scholar 

  162. 162.

    Bottou, L. in Proceedings of Neuro-Nımes ‘91 12 (EC2, 1991).

  163. 163.

    Bengio, Y. Practical recommendations for gradient-based training of deep architectures. Preprint at arXiv https://arxiv.org/abs/1206.5533 (2012).

  164. 164.

    Bergstra, J. & Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281–305 (2012).

    Google Scholar 

  165. 165.

    Bergstra, J., Yamins, D. & Cox, D. in Proceedings of the 30th International Conference on Machine Learning Vol. 28 115–123 (JMLR W&CP, 2013).

  166. 166.

    Shahriari, B., Swersky, K., Wang, Z., Adams, R. P. & de Freitas, N. Taking the human out of the loop: a review of bayesian optimization. Proc. IEEE 104, 148–175 (2016).

    Google Scholar 

  167. 167.

    Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A. & Talwalkar, A. Hyperband: a novel bandit-based approach to hyperparameter optimization. J. Mach. Learn. Res. 18, 6765–6816 (2017).

    Google Scholar 

  168. 168.

    Elsken, T., Metzen, J. H. & Hutter, F. Neural architecture search: a survey. Preprint at arXiv https://arxiv.org/abs/1808.05377 (2018).

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Acknowledgements

Ž.A. was supported by the German Bundesministerium für Bildung und Forschung (BMBF) through the project MechML (01IS18053F). The authors acknowledge M. Heinig and A. Raue for valuable feedback.

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Nature Reviews Genetics thanks C. Greene and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Correspondence to Julien Gagneur or Fabian J. Theis.

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Glossary

Feature

An individual, measurable property or characteristic of a phenomenon being observed.

Handcrafted features

Features derived from raw data (or other features) using manually specified rules. Unlike learned features, they are specified upfront and do not change during model training. For example, the GC content is a handcrafted feature of a DNA sequence.

End-to-end models

Machine learning models that embed the entire data-processing pipeline to transform raw input data into predictions without requiring a preprocessing step.

Deep neural networks

A wide class of machine learning models with a design that is loosely based on biological neural networks.

Fully connected

Referring to a layer that performs an affine transformation of a vector followed by application of an activation function to each value.

Convolutional

Referring to a neural network layer that processes data stored in n-dimensional arrays, such as images. The same fully connected layer is applied to multiple local patches of the input array. When applied to DNA sequences, a convolutional layer can be interpreted as a set of position weight matrices scanned across the sequence.

Recurrent

Referring to a neural network layer that processes sequential data. The same neural network is applied at each step of the sequence and updates a memory variable that is provided for the next step.

Graph convolutional

Referring to neural networks that process graph-structured data; they generalize convolution beyond regular structures, such as DNA sequences and images, to graphs with arbitrary structures. The same neural network is applied to each node and edge in the graph.

Autoencoders

Unsupervised neural networks trained to reconstruct the input. One or more bottleneck layers have lower dimensionality than the input, which leads to compression of data and forces the autoencoder to extract useful features and omit unimportant features in the reconstruction.

Generative adversarial networks

(GANs). Unsupervised learning models that aim to generate data points that are indistinguishable from the observed ones.

Target

The desired output used to train a supervised model.

Loss function

A function that is optimized during training to fit machine learning model parameters. In the simplest case, it measures the discrepancy between predictions and observations. In the case of quantitative predictions such as regression, mean-squared error loss is frequently used, and for binary classification, the binary cross-entropy, also called logistic loss, is typically used.

k-mer

Character sequence of a certain length. For instance, a dinucleotide is a k-mer for which k = 2.

Logistic regression

A supervised learning algorithm that predicts the log-odds of a binary output to be of the positive class as a weighted sum of the input features. Transformation of the log-odds with the sigmoid activation function leads to predicted probabilities.

Sigmoid function

A function that maps real numbers to [0,1], defined as 1/(1 + e −x).

Activation function

A function applied to an intermediate value x within a neural network. Activation functions are usually nonlinear yet very simple, such as the rectified-linear unit or the sigmoid function.

Regularization

A strategy to prevent overfitting that is typically achieved by constraining the model parameters during training by modifying the loss function or the parameter optimization procedure. For example, the so-called L2 regularization adds the sum of the squares of the model parameters to the loss function to penalize large model parameters.

Hidden layers

Layers are a list of artificial neurons that collectively represents a function that take as input an array of real numbers and returns an array of real numbers corresponding to neuron activations. Hidden layers are between the input and output layers.

Rectified-linear unit

(ReLU). Widely used activation function defined as max(0, x).

Neuron

The elementary unit of a neural network. An artificial neuron aggregates the inputs from other neurons and emits an output called activation. Inputs and activations of artificial neurons are real numbers. The activation of an artificial neuron is computed by applying a nonlinear activation function to a weighted sum of its inputs.

Linear regression

A supervised learning algorithm that predicts the output as a weighted sum of the input features.

Decision trees

Supervised learning algorithms in which the prediction is made by making a series of decisions of type ‘is feature i larger than x’ (internal nodes of the tree) and then predicting a constant value for all points satisfying the same decisions series (leaf nodes).

Random forests

Supervised learning algorithms that train and average the predictions of many decision trees.

Gradient-boosted decision trees

Supervised learning algorithms that train multiple decision trees in a sequential manner; at each time step, a new decision tree is trained on the residual or pseudo-residual of the previous decision tree.

Position weight matrix

(PWM). A commonly used representation of sequence motifs in biological sequences. It is based on nucleotide frequencies of aligned sequences at each position and can be used for identifying transcription factor binding sites from DNA sequence.

Overfitting

The scenario in which the model fits the training set very well but does not generalize well to unseen data. Very flexible models with many free parameters are prone to overfitting, whereas models with many fewer parameters than the training data do not overfit.

Filters

Parameters of a convolutional layer. In the first layer of a sequence-based convolutional network, they can be interpreted as position weight matrices.

Pooling operation

A function that replaces the output at a certain location with a summary statistic of the nearby outputs. For example, the max pooling operation reports the maximum output within a rectangular neighbourhood.

Channel

An axis other than one of the positional axes. For images, the channel axis encodes different colours (such as red, green and blue), for one-hot-encoded sequences (A: [1, 0, 0, 0], C: [0, 1, 0, 0] and so on), it denotes the bases (A, C, G and T), and for the output of the convolutions, it corresponds to the outputs of different filters.

Dilated convolutions

Filters that skip some values in the input layers. Typically, each subsequent convolutional layer increases the dilation by a factor of two, thus achieving an exponentially increasing receptive field with each additional layer.

Receptive field

The region of the input that affects the output of a convolutional neuron.

Memory

An array that stores the information of the patterns observed in the sequence elements previously processed by a recurrent neural network.

Feature importance scores

The quantification values of the contributions of features to a current model prediction. The simplest way to obtain this score is to perturb the feature value and measure the change in the model prediction: the larger the change found, the more important the feature is.

Backpropagation

An algorithm for computing gradients of neural networks. Gradients with respect to the loss function are used to update the neural network parameters during training.

Saliency maps

Feature importance scores defined as the gradient absolute values of the model output with respect to the model input.

Input-masked gradients

Feature importance scores defined as the gradient of the model output with respect to the model input multiplied by the input values.

Automatic differentiation

A set of techniques, which consist of a sequence of elementary arithmetic operations, used to automatically differentiate a computer program.

Model architecture

The structure of a neural network independent of its parameter values. Important aspects of model architecture are the types of layers, their dimensions and how they are connected to each other.

k-means

An unsupervised method for partitioning the observations into clusters by alternating between refining cluster centroids and updating cluster assignments of observations.

Principal component analysis

An unsupervised learning algorithm that linearly projects data from a high-dimensional space to a lower-dimensional space while retaining as much variance as possible.

t-Distributed stochastic neighbour embedding

(t-SNE). An unsupervised learning algorithm that projects data from a high-dimensional space to a lower-dimensional space (typically 2D or 3D) in a nonlinear fashion while trying to preserve the distances between points.

Latent variable models

Unsupervised models describing the observed distribution by imposing latent (unobserved) variables for each data point. The simplest example is the mixture of Gaussian values.

Bottleneck layer

A neural network layer that contains fewer neurons than previous and subsequent layers.

Generative models

Models able to generate points from the desired distribution. Deep generative models are often implemented by a neural network that transforms samples from a standard distribution (normal and uniform) into samples from a complex distribution (gene expression levels or sequences that encode a splice site).

Hyperparameters

Parameters specifying the model or the training procedure that are not optimized by the learning algorithm (for example, by the stochastic gradient descent algorithm). Examples of hyperparameters are the number of layers, regularization strength, batch size and the optimization step size.

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Eraslan, G., Avsec, Ž., Gagneur, J. et al. Deep learning: new computational modelling techniques for genomics. Nat Rev Genet 20, 389–403 (2019). https://doi.org/10.1038/s41576-019-0122-6

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