1932

Abstract

Deep neural networks have been revolutionizing the field of machine learning for the past several years. They have been applied with great success in many domains of the biomedical data sciences and are outperforming extant methods by a large margin. The ability of deep neural networks to pick up local image features and model the interactions between them makes them highly applicable to regulatory genomics. Instead of an image, the networks analyze DNA and RNA sequences and additional epigenomic data. In this review, we survey the successes of deep learning in the field of regulatory genomics. We first describe the fundamental building blocks of deep neural networks, popular architectures used in regulatory genomics, and their training process on molecular sequence data. We then review several key methods in different gene regulation domains. We start with the pioneering method DeepBind and its successors, which were developed to predict protein–DNA binding. We then review methods developed to predict and model epigenetic information, such as histone marks and nucleosome occupancy. Following epigenomics, we review methods to predict protein–RNA binding with its unique challenge of incorporating RNA structure information. Finally, we provide our overall view of the strengths and weaknesses of deep neural networks and prospects for future developments.

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2020-07-20
2024-03-29
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Literature Cited

  1. 1. 
    Lodish H, Berk A, Kaiser CA, Krieger M, Bretscher A et al. 2016. Molecular Cell Biology New York: Macmillan. , 8th ed..
  2. 2. 
    Crick F. 1970. Central dogma of molecular biology. Nature 227:561–63
    [Google Scholar]
  3. 3. 
    Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z et al. 2002. Transcriptional regulatory networks in Saccharomyces cerevisiae. . Science 298:799–804
    [Google Scholar]
  4. 4. 
    Jiang C, Pugh BF. 2009. Nucleosome positioning and gene regulation: advances through genomics. Nat. Rev. Genet. 10:161–72
    [Google Scholar]
  5. 5. 
    Ernst J, Kellis M. 2012. ChromHMM: automating chromatin-state discovery and characterization. Nat. Methods 9:215–16
    [Google Scholar]
  6. 6. 
    Attwood J, Yung R, Richardson B 2002. DNA methylation and the regulation of gene transcription. Cell. Mol. Life Sci. 59:241–57
    [Google Scholar]
  7. 7. 
    Glisovic T, Bachorik JL, Yong J, Dreyfuss G 2008. RNA-binding proteins and post-transcriptional gene regulation. FEBS Lett 582:1977–86
    [Google Scholar]
  8. 8. 
    Hardison RC, Taylor J. 2012. Genomic approaches towards finding cis-regulatory modules in animals. Nat. Rev. Genet. 13:469–83
    [Google Scholar]
  9. 9. 
    Andersson R, Sandelin A. 2019. Determinants of enhancer and promoter activities of regulatory elements. Nat. Rev. Genet. 21:71–87
    [Google Scholar]
  10. 10. 
    Gaszner M, Felsenfeld G. 2006. Insulators: exploiting transcriptional and epigenetic mechanisms. Nat. Rev. Genet. 7:703–13
    [Google Scholar]
  11. 11. 
    Yue F, Cheng Y, Breschi A, Vierstra J, Wu W et al. 2014. A comparative encyclopedia of DNA elements in the mouse genome. Nature 515:355–64
    [Google Scholar]
  12. 12. 
    Lambert SA, Jolma A, Campitelli LF, Das PK, Yin Y et al. 2018. The human transcription factors. Cell 172:650–65
    [Google Scholar]
  13. 13. 
    Neelamraju Y, Gonzalez-Perez A, Bhat-Nakshatri P, Nakshatri H, Janga SC 2018. Mutational landscape of RNA-binding proteins in human cancers. RNA Biol 15:115–29
    [Google Scholar]
  14. 14. 
    Li Y, Hu M, Shen Y 2018. Gene regulation in the 3D genome. Hum. Mol. Genet. 27:R228–33
    [Google Scholar]
  15. 15. 
    Lesurf R, Cotto KC, Wang G, Griffith M, Kasaian K et al. 2016. ORegAnno 3.0: a community-driven resource for curated regulatory annotation. Nucleic Acids Res 44:D126–32
    [Google Scholar]
  16. 16. 
    Furey TS. 2012. ChIP–seq and beyond: new and improved methodologies to detect and characterize protein–DNA interactions. Nat. Rev. Genet. 13:840–52
    [Google Scholar]
  17. 17. 
    Tsompana M, Buck MJ. 2014. Chromatin accessibility: a window into the genome. Epigenet. Chromatin 7:33
    [Google Scholar]
  18. 18. 
    Bibikova M, Fan JB. 2010. Genome-wide DNA methylation profiling. Wiley Interdiscip. Rev. Syst. Biol. Med. 2:210–23
    [Google Scholar]
  19. 19. 
    Clark SJ, Smallwood SA, Lee HJ, Krueger F, Reik W, Kelsey G 2017. Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq). Nat. Protoc. 12:534–47
    [Google Scholar]
  20. 20. 
    Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H et al. 2014. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11:817–20
    [Google Scholar]
  21. 21. 
    Li N, Ye M, Li Y, Yan Z, Butcher LM et al. 2010. Whole genome DNA methylation analysis based on high throughput sequencing technology. Methods 52:203–12
    [Google Scholar]
  22. 22. 
    Marchese D, de Groot NS, Lorenzo Gotor N, Livi CM, Tartaglia GG 2016. Advances in the characterization of RNA-binding proteins. Wiley Interdiscip. Rev. RNA 7:793–810
    [Google Scholar]
  23. 23. 
    Berger MF, Philippakis AA, Qureshi AM, He FS, Estep PW 3rd, Bulyk ML 2006. Compact, universal DNA microarrays to comprehensively determine transcription-factor binding site specificities. Nat. Biotechnol. 24:1429–35
    [Google Scholar]
  24. 24. 
    Jolma A, Kivioja T, Toivonen J, Cheng L, Wei G et al. 2010. Multiplexed massively parallel SELEX for characterization of human transcription factor binding specificities. Genome Res 20:861–73
    [Google Scholar]
  25. 25. 
    Buenrostro JD, Wu B, Chang HY, Greenleaf WJ 2015. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109:21–29
    [Google Scholar]
  26. 26. 
    Song L, Crawford GE. 2010. DNase-seq: a high-resolution technique for mapping active gene regulatory elements across the genome from mammalian cells. Cold Spring Harb. Protoc. https://www.doi.org/10.1101/pdb.prot5384
    [Crossref] [Google Scholar]
  27. 27. 
    Ray D, Kazan H, Chan ET, Castillo LP, Chaudhry S et al. 2009. Rapid and systematic analysis of the RNA recognition specificities of RNA-binding proteins. Nat. Biotechnol. 27:667–70
    [Google Scholar]
  28. 28. 
    Lambert N, Robertson A, Jangi M, McGeary S, Sharp PA, Burge CB 2014. RNA Bind-n-Seq: quantitative assessment of the sequence and structural binding specificity of RNA binding proteins. Mol. Cell 54:887–900
    [Google Scholar]
  29. 29. 
    Hashim FA, Mabrouk MS, Al-Atabany W 2019. Review of different sequence motif finding algorithms. Avicenna J. Med. Biotechnol. 11:130–48
    [Google Scholar]
  30. 30. 
    Leibovich L, Yakhini Z. 2014. Mutual enrichment in ranked lists and the statistical assessment of position weight matrix motifs. Algorithms Mol. Biol. 9:11
    [Google Scholar]
  31. 31. 
    Alipanahi B, Delong A, Weirauch MT, Frey BJ 2015. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33:831–38
    [Google Scholar]
  32. 32. 
    Narlikar L, Gordân R, Hartemink AJ 2007. Nucleosome occupancy information improves de novo motif discovery. Proceedings of the 11th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2007) T Speed, H Huang 107–21 Cham, Switz: Springer
    [Google Scholar]
  33. 33. 
    Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS et al. 2016. ENCODE data at the ENCODE portal. Nucleic Acids Res 44:D726–32
    [Google Scholar]
  34. 34. 
    Bernstein BE, Stamatoyannopoulos JA, Costello JF, Ren B, Milosavljevic A et al. 2010. The NIH Roadmap Epigenomics Mapping Consortium. Nat. Biotechnol. 28:1045–48
    [Google Scholar]
  35. 35. 
    Hume MA, Barrera LA, Gisselbrecht SS, Bulyk ML 2015. UniPROBE, update 2015: new tools and content for the online database of protein-binding microarray data on protein–DNA interactions. Nucleic Acids Res 43:D117–22
    [Google Scholar]
  36. 36. 
    Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-Tárraga A et al. 2010. The European Nucleotide Archive. Nucleic Acids Res 39:D28–31
    [Google Scholar]
  37. 37. 
    Weirauch MT, Yang A, Albu M, Cote AG, Montenegro-Montero A et al. 2014. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158:1431–43
    [Google Scholar]
  38. 38. 
    Blin K, Dieterich C, Wurmus R, Rajewsky N, Landthaler M, Akalin A 2015. DoRiNA 2.0—upgrading the doRiNA database of RNA interactions in post-transcriptional regulation. Nucleic Acids Res 43:D160–67
    [Google Scholar]
  39. 39. 
    Li JH, Liu S, Zhou H, Qu LH, Yang JH 2014. starBase v2.0: decoding miRNA–ceRNA, miRNA–ncRNA and protein–RNA interaction networks from large-scale CLIP-seq data. Nucleic Acids Res 42:D92–97
    [Google Scholar]
  40. 40. 
    Yang YCT, Di C, Hu B, Zhou M, Liu Y et al. 2015. CLIPdb: a CLIP-seq database for protein-RNA interactions. BMC Genom 16:51
    [Google Scholar]
  41. 41. 
    Ray D, Kazan H, Cook KB, Weirauch MT, Najafabadi HS et al. 2013. A compendium of RNA-binding motifs for decoding gene regulation. Nature 499:172–77
    [Google Scholar]
  42. 42. 
    Kodama Y, Shumway M, Leinonen R 2012. The Sequence Read Archive: explosive growth of sequencing data. Nucleic Acids Res 40:D54–56
    [Google Scholar]
  43. 43. 
    Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D et al. 2007. NCBI GEO: mining tens of millions of expression profiles—database and tools update. Nucleic Acids Res 35:D760–65
    [Google Scholar]
  44. 44. 
    Grunau C, Renault E, Rosenthal A, Roizes G 2001. MethDB—a public database for DNA methylation data. Nucleic Acids Res 29:270–74
    [Google Scholar]
  45. 45. 
    LeCun Y, Bengio Y, Hinton G 2015. Deep learning. Nature 521:436–44
    [Google Scholar]
  46. 46. 
    Min S, Lee B, Yoon S 2017. Deep learning in bioinformatics. Brief. Bioinform. 18:851–69
    [Google Scholar]
  47. 47. 
    Nair V, Hinton GE. 2010. Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10) J Fürnkranz, T Joachims 807–14 Madison, WI: Omnipress
    [Google Scholar]
  48. 48. 
    Hirohara M, Saito Y, Koda Y, Sato K, Sakakibara Y 2018. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. BMC Bioinform 19:526
    [Google Scholar]
  49. 49. 
    Shen Z, Bao W, Huang DS 2018. Recurrent neural network for predicting transcription factor binding sites. Sci. Rep. 8:15270
    [Google Scholar]
  50. 50. 
    Graves A, Mohamed A-R, Hinton G 2013. Speech recognition with deep recurrent neural networks. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing6645–49 New York: IEEE
    [Google Scholar]
  51. 51. 
    Hassanzadeh HR, Wang MD. 2016. DeeperBind: enhancing prediction of sequence specificities of DNA binding proteins. Proceedings of the 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)178–83 New York: IEEE
    [Google Scholar]
  52. 52. 
    Tang D, Qin B, Liu T 2015. Document modeling with gated recurrent neural network for sentiment classification. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing L Màrquez, C Callison-Burch, J Su 1422–32 Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  53. 53. 
    Schmidhuber J. 2015. Deep learning in neural networks: an overview. Neural Netw 61:85–117
    [Google Scholar]
  54. 54. 
    Ling W, Dyer C, Black AW, Trancoso I 2015. Two/too simple adaptations of Word2vec for syntax problems. Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies R Mihalcea, J Chai, A Sarkar 1299–304 Stroudsburg, PA: Assoc. Comput. Linguist.
    [Google Scholar]
  55. 55. 
    Schuster M, Paliwal KK. 1997. Bidirectional recurrent neural networks. IEEE Trans. Signal Proc. 45:2673–81
    [Google Scholar]
  56. 56. 
    Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Comput 9:1735–80
    [Google Scholar]
  57. 57. 
    Chung J, Gulcehre C, Cho K, Bengio Y 2015. Gated feedback recurrent neural networks. Proceedings of the 32nd International Conference on Machine Learning F Bach, D Blei 2067–75 New York: Assoc. Comput. Mach.
    [Google Scholar]
  58. 58. 
    Liou CY, Cheng WC, Liou JW, Liou DR 2014. Autoencoder for words. Neurocomputing 139:84–96
    [Google Scholar]
  59. 59. 
    Goodfellow I, Bengio Y, Courville A 2016. Deep Learning Cambridge, MA: MIT Press
  60. 60. 
    Vincent P, Larochelle H, Bengio Y, Manzagol PA 2008. Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning1096–103 New York: Assoc. Comput. Mach.
    [Google Scholar]
  61. 61. 
    Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L et al. 2017. Attention is all you need. Proceedings of the 30th International Conference on Advances in Neural Information Processing Systems (NIPS 2017) I Guyon, UV Luxburg, S Bengio, H Wallach, R Fergus et al. https://papers.nips.cc/paper/7181-attention-is-all-you-need.pdf
  62. 62. 
    Bahdanau D, Cho K, Bengio Y 2014. Neural machine translation by jointly learning to align and translate. arXiv1409.0473 [cs.CL]
  63. 63. 
    Xu K, Ba J, Kiros R, Cho K, Courville A et al. 2015. Show, attend and tell: neural image caption generation with visual attention. Proceedings of the 32nd International Conference on Machine Learning F Bach, D Blei 2048–57 New York: Assoc. Comput. Mach.
    [Google Scholar]
  64. 64. 
    Hinton GE. 2009. Deep belief networks. Scholarpedia 4:5947
    [Google Scholar]
  65. 65. 
    Sutskever I, Hinton GE, Taylor GW 2009. The recurrent temporal restricted Boltzmann machine. Proceedings of the 21st International Conference on Advances in Neural Information Processing Systems (NIPS 2008) D Koller, D Schuurmans, Y Bengio, L Bottou. https://papers.nips.cc/paper/3567-the-recurrent-temporal-restricted-boltzmann-machine
  66. 66. 
    Ruder S. 2016. An overview of gradient descent optimization algorithms. arXiv1609.04747 [cs.LG]
  67. 67. 
    Ben-Bassat I, Chor B, Orenstein Y 2018. A deep neural network approach for learning intrinsic protein-RNA binding preferences. Bioinformatics 34:i638–46
    [Google Scholar]
  68. 68. 
    Zeng H, Edwards MD, Liu G, Gifford DK 2016. Convolutional neural network architectures for predicting DNA-protein binding. Bioinformatics 32:i121–27
    [Google Scholar]
  69. 69. 
    Quang D, Xie X. 2016. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res 44:e107
    [Google Scholar]
  70. 70. 
    Zhang Q, Shen Z, Huang DS 2019. Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network. Sci. Rep. 9:8484
    [Google Scholar]
  71. 71. 
    Zhou J, Theesfeld CL, Yao K, Chen KM, Wong AK, Troyanskaya OG 2018. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet. 50:1171–79
    [Google Scholar]
  72. 72. 
    Sundararajan M, Taly A, Yan Q 2017. Axiomatic attribution for deep networks. Proceedings of the 34th International Conference on Machine Learning D Precup, YW Teh 3319–28 New York: Assoc. Comput. Mach.
    [Google Scholar]
  73. 73. 
    Shrikumar A, Greenside P, Kundaje A 2017. Learning important features through propagating activation differences. Proceedings of the 34th International Conference on Machine Learning D Precup, YW Teh 3145–53 New York: Assoc. Comput. Mach.
    [Google Scholar]
  74. 74. 
    Ribeiro MT, Singh S, Guestrin C 2016. Why should I trust you? Explaining the predictions of any classifier. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining1135–44 New York: Assoc. Comput. Mach.
    [Google Scholar]
  75. 75. 
    Zhou J, Troyanskaya OG. 2015. Predicting effects of noncoding variants with deep learning–based sequence model. Nat. Methods 12:931–34
    [Google Scholar]
  76. 76. 
    Deleted in proof
  77. 77. 
    Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A et al. 2015. Integrative analysis of 111 reference human epigenomes. Nature 518:317–30
    [Google Scholar]
  78. 78. 
    Angermueller C, Lee HJ, Reik W, Stegle O 2017. DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning. Genome Biol 18:67
    [Google Scholar]
  79. 79. 
    Min X, Chen N, Chen T, Jiang R 2016. DeepEnhancer: predicting enhancers by convolutional neural networks. Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM) T Tian, Y Wang, Q Jiang, X Hu, Y Liu et al.637–44 New York: IEEE
    [Google Scholar]
  80. 80. 
    Bock C. 2012. Analysing and interpreting DNA methylation data. Nat. Rev. Genet. 13:705–19
    [Google Scholar]
  81. 81. 
    Tian Q, Zou J, Tang J, Fang Y, Yu Z, Fan S 2019. MRCNN: a deep learning model for regression of genome-wide DNA methylation. BMC Genom 20:192
    [Google Scholar]
  82. 82. 
    Wang Y, Liu T, Xu D, Shi H, Zhang C et al. 2016. Predicting DNA methylation state of CpG dinucleotide using genome topological features and deep networks. Sci. Rep. 6:19598
    [Google Scholar]
  83. 83. 
    Levy-Jurgenson A, Tekpli X, Kristensen VN, Yakhini Z 2019. Predicting methylation from sequence and gene expression using deep learning with attention. Proceedings of the 6th International Conference on Algorithms for Computational Biology I Holmes, C Martín-Vide, MA Vega-Rodríguez 179–90 Cham, Switz: Springer
    [Google Scholar]
  84. 84. 
    Yang B, Liu F, Ren C, Ouyang Z, Xie Z et al. 2017. BiRen: predicting enhancers with a deep-learning-based model using the DNA sequence alone. Bioinformatics 33:1930–36
    [Google Scholar]
  85. 85. 
    Min X, Zeng W, Chen S, Chen N, Chen T, Jiang R 2017. Predicting enhancers with deep convolutional neural networks. BMC Bioinform 18:478
    [Google Scholar]
  86. 86. 
    Liu F, Li H, Ren C, Bo X, Shu W 2016. PEDLA: predicting enhancers with a deep learning-based algorithmic framework. Sci. Rep. 6:28517
    [Google Scholar]
  87. 87. 
    Bu H, Gan Y, Wang Y, Zhou S, Guan J 2017. A new method for enhancer prediction based on deep belief network. BMC Bioinform 18:418
    [Google Scholar]
  88. 88. 
    Wheeler EC, Van Nostrand EL, Yeo GW 2018. Advances and challenges in the detection of transcriptome-wide protein–RNA interactions. Wiley Interdiscip. Rev. RNA 9:e1436
    [Google Scholar]
  89. 89. 
    Pan X, Yang Y, Xia CQ, Mirza AH, Shen HB 2019. Recent methodology progress of deep learning for RNA–protein interaction prediction. Wiley Interdiscip. Rev. RNA 10:6e1544
    [Google Scholar]
  90. 90. 
    Pan X, Shen HB. 2017. RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach. BMC Bioinform 18:136
    [Google Scholar]
  91. 91. 
    Avsec Ž, Barekatain M, Cheng J, Gagneur J 2017. Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks. Bioinformatics 34:1261–69
    [Google Scholar]
  92. 92. 
    Pan X, Yan J. 2017. Attention based convolutional neural network for predicting RNA-protein binding sites. arXiv1712.02270 [q-bio.GN]
  93. 93. 
    Pan X, Shen HB. 2018. Learning distributed representations of RNA sequences and its application for predicting RNA-protein binding sites with a convolutional neural network. Neurocomputing 305:51–58
    [Google Scholar]
  94. 94. 
    Pan X, Rijnbeek P, Yan J, Shen HB 2018. Prediction of RNA-protein sequence and structure binding preferences using deep convolutional and recurrent neural networks. BMC Genom 19:511
    [Google Scholar]
  95. 95. 
    Lorenz R, Bernhart SH, zu Siederdissen CH, Tafer H, Flamm C et al. 2011. ViennaRNA package 2.0. Algorithms Mol. Biol. 6:26
    [Google Scholar]
  96. 96. 
    Steffen P, Voß B, Rehmsmeier M, Reeder J, Giegerich R 2005. RNAshapes: an integrated RNA analysis package based on abstract shapes. Bioinformatics 22:500–3
    [Google Scholar]
  97. 97. 
    Maticzka D, Lange SJ, Costa F, Backofen R 2014. GraphProt: modeling binding preferences of RNA-binding proteins. Genome Biol 15:R17
    [Google Scholar]
  98. 98. 
    Budach S, Marsico A. 2018. pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks. Bioinformatics 34:3035–37
    [Google Scholar]
  99. 99. 
    Chakrabarti AM, Haberman N, Praznik A, Luscombe NM, Ule J 2018. Data science issues in studying protein–RNA interactions with CLIP technologies. Annu. Rev. Biomed. Data Sci. 1:235–61
    [Google Scholar]
  100. 100. 
    Gandhi S, Lee LJ, Delong A, Duvenaud D, Frey B 2018. cDeepbind: a context sensitive deep learning model of RNA-protein binding. bioRxiv 345140. https://doi.org/10.1101/345140
    [Crossref]
  101. 101. 
    Orenstein Y, Wang Y, Berger B 2016. RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data. Bioinformatics 32:i351–59
    [Google Scholar]
  102. 102. 
    Li Z, Zhu J, Xu X, Yao Y 2019. RDense: a protein-RNA binding prediction model based on bidirectional recurrent neural network and densely connected convolutional networks. IEEE Access 8:14588–605
    [Google Scholar]
  103. 103. 
    Livi CM, Blanzieri E. 2014. Protein-specific prediction of mRNA binding using RNA sequences, binding motifs and predicted secondary structures. BMC Bioinform 15:123
    [Google Scholar]
  104. 104. 
    Pan X, Fan YX, Yan J, Shen HB 2016. IPMiner: hidden ncRNA-protein interaction sequential pattern mining with stacked autoencoder for accurate computational prediction. BMC Genom 17:582
    [Google Scholar]
  105. 105. 
    Zhang S, Zhou J, Hu H, Gong H, Chen L et al. 2015. A deep learning framework for modeling structural features of RNA-binding protein targets. Nucleic Acids Res 44:e32
    [Google Scholar]
  106. 106. 
    Su Y, Luo Y, Zhao X, Liu Y, Peng J 2019. Integrating thermodynamic and sequence contexts improves protein-RNA binding prediction. PLOS Comput. Biol. 15:e1007283
    [Google Scholar]
  107. 107. 
    Eraslan G, Avsec Ž, Gagneur J, Theis FJ 2019. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20:389–403
    [Google Scholar]
  108. 108. 
    Wainberg M, Merico D, Delong A, Frey BJ 2018. Deep learning in biomedicine. Nat. Biotechnol. 36:829–38
    [Google Scholar]
  109. 109. 
    Zou J, Huss M, Abid A, Mohammadi P, Torkamani A, Telenti A 2018. A primer on deep learning in genomics. Nat. Genet. 51:12–18
    [Google Scholar]
  110. 110. 
    Lan K, Wang D-T, Fong S, Liu L-S, Wong KK, Dey N 2018. A survey of data mining and deep learning in bioinformatics. J. Med. Syst. 42:139
    [Google Scholar]
  111. 111. 
    Cao C, Liu F, Tan H, Song D, Shu W et al. 2018. Deep learning and its applications in biomedicine. Genom. Proteom. Bioinform. 16:17–32
    [Google Scholar]
  112. 112. 
    Peng L, Peng M, Liao B, Huang G, Li W, Xie D 2018. The advances and challenges of deep learning application in biological big data processing. Curr. Bioinform. 13:352–59
    [Google Scholar]
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