Next-Generation Machine Learning for Biological Networks

Cell

Volumn 173, Issue 7, 2018, Pages 1581-1592

  Camacho, Diogo M ^a   Collins, Katherine M ^a,b   Powers, Rani K ^c   Costello, James C ^c   Collins, James J ^a,d,e  

^a HARVARD UNIVERSITY   (United States)

^b USA   (United States)

^c UNIVERSITY OF COLORADO SCHOOL OF MEDICINE   (United States)

^d Massachusetts Institute of Technology Cambridge   (United States)

^e BROAD INSTITUTE OF MIT AND HARVARD   (United States)

Author keywords

deep learning; Machine leaning; network biology; neural networks; synthetic biology; systems biology

Indexed keywords

BIOLOGICAL NETWORK; DRUG DEVELOPMENT; GENE INTERACTION; HUMAN; INFORMATION PROCESSING; LEARNING ALGORITHM; MACHINE LEARNING; MICROBIAL COMMUNITY; MICROBIAL METABOLISM; MICROBIOME; MOLECULAR BIOLOGY; NERVE CELL NETWORK; NEXT GENERATION MACHINE LEARNING; NONHUMAN; PRIORITY JOURNAL; PROTEIN PROTEIN INTERACTION; REVIEW; SYNTHETIC BIOLOGY; ADVERSE DRUG REACTION; ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGY; FACTUAL DATABASE; MICROFLORA; PROCEDURES;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, FACTUAL; DRUG DISCOVERY; DRUG-RELATED SIDE EFFECTS AND ADVERSE REACTIONS; HUMANS; MACHINE LEARNING; MICROBIOTA; NEURAL NETWORKS (COMPUTER);

EID: 85047752833     PISSN: 00928674     EISSN: 10974172     Source Type: Journal    
DOI: 10.1016/j.cell.2018.05.015     Document Type: Review

References (100)

1. Abdi, H., Williams, L.J., Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2 (2010), 433–459.
2. Akavia, U.D., Litvin, O., Kim, J., Sanchez-Garcia, F., Kotliar, D., Causton, H.C., Pochanard, P., Mozes, E., Garraway, L.A., Pe'er, D., An integrated approach to uncover drivers of cancer. Cell 143 (2010), 1005–1017.
3. 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 (2015), 831–838.
4. Ammad-Ud-Din, M., Khan, S.A., Wennerberg, K., Aittokallio, T., Systematic identification of feature combinations for predicting drug response with Bayesian multi-view multi-task linear regression. Bioinformatics 33 (2017), i359–i368.
5. Anderson, E., Veith, G.D., Weininger, D., SMILES: a line notation and computerized interpreter for chemical structures. 1987, U.S. Environmental Protection Agency, Environmental Research Laboratory.
6. Angermueller, C., Pärnamaa, T., Parts, L., Stegle, O., Deep learning for computational biology. Mol. Syst. Biol., 12, 2016, 878.
7. Ballester, P.J., Mitchell, J.B., A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics 26 (2010), 1169–1175.
8. Bansal, M., Yang, J., Karan, C., Menden, M.P., Costello, J.C., Tang, H., Xiao, G., Li, Y., Allen, J., Zhong, R., et al. NCI-DREAM Community, NCI-DREAM Community. A community computational challenge to predict the activity of pairs of compounds. Nat. Biotechnol. 32 (2014), 1213–1222.
9. Barabási, A.-L., Oltvai, Z.N., Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5 (2004), 101–113.
10. Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A.A., Kim, S., Wilson, C.J., Lehár, J., Kryukov, G.V., Sonkin, D., et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483 (2012), 603–607.
11. Basu, A., Bodycombe, N.E., Cheah, J.H., Price, E.V., Liu, K., Schaefer, G.I., Ebright, R.Y., Stewart, M.L., Ito, D., Wang, S., et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154 (2013), 1151–1161.
12. Bisikirska, B., Bansal, M., Shen, Y., Teruya-Feldstein, J., Chaganti, R., Califano, A., Elucidation and Pharmacological Targeting of Novel Molecular Drivers of Follicular Lymphoma Progression. Cancer Res. 76 (2016), 664–674.
13. Bordbar, A., Palsson, B.O., Using the reconstructed genome-scale human metabolic network to study physiology and pathology. J. Intern. Med. 271 (2012), 131–141.
14. Butte, A.J., Kohane, I.S., Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput., 2000, 418–429.
15. Cahan, P., Li, H., Morris, S.A., Lummertz da Rocha, E., Daley, G.Q., Collins, J.J., CellNet: network biology applied to stem cell engineering. Cell 158 (2014), 903–915.
16. Califano, A., Alvarez, M.J., The recurrent architecture of tumour initiation, progression and drug sensitivity. Nat. Rev. Cancer 17 (2017), 116–130.
17. Califano, A., Butte, A.J., Friend, S., Ideker, T., Schadt, E., Leveraging models of cell regulation and GWAS data in integrative network-based association studies. Nat. Genet. 44 (2012), 841–847.
18. Cameron, D.E., Bashor, C.J., Collins, J.J., A brief history of synthetic biology. Nat. Rev. Microbiol. 12 (2014), 381–390.
19. Carro, M.S., Lim, W.K., Alvarez, M.J., Bollo, R.J., Zhao, X., Snyder, E.Y., Sulman, E.P., Anne, S.L., Doetsch, F., Colman, H., et al. The transcriptional network for mesenchymal transformation of brain tumours. Nature 463 (2010), 318–325.
20. Caruana, R., Multitask Learning. Learning to Learn, 1998, Springer, 95–133.
21. Chandrashekar, G., Sahin, F., A survey on feature selection methods. Comput. Electr. Eng. 40 (2014), 16–28.
22. Chatr-Aryamontri, A., Oughtred, R., Boucher, L., Rust, J., Chang, C., Kolas, N.K., O'Donnell, L., Oster, S., Theesfeld, C., Sellam, A., et al. The BioGRID interaction database: 2017 update. Nucleic Acids Res. 45:D1 (2017), D369–D379.
23. Chen, B., Butte, A.J., Leveraging big data to transform target selection and drug discovery. Clin. Pharmacol. Ther. 99 (2016), 285–297.
24. Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., Way, G.P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M.M., et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface, 15, 2018.
25. Chuang, H.-Y., Lee, E., Liu, Y.-T., Lee, D., Ideker, T., Network-based classification of breast cancer metastasis. Mol. Syst. Biol., 3, 2007, 140.
26. Costello, J.C., Stolovitzky, G., Seeking the wisdom of crowds through challenge-based competitions in biomedical research. Clin. Pharmacol. Ther. 93 (2013), 396–398.
27. Costello, J.C., Heiser, L.M., Georgii, E., Gönen, M., Menden, M.P., Wang, N.J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., Khan, S.A., et al., NCI DREAM Community. A community effort to assess and improve drug sensitivity prediction algorithms. Nat. Biotechnol. 32 (2014), 1202–1212.
28. de la Fuente, A., From ‘differential expression’ to ‘differential networking’ - identification of dysfunctional regulatory networks in diseases. Trends Genet. 26 (2010), 326–333.
29. De Smet, R., Marchal, K., Advantages and limitations of current network inference methods. Nat. Rev. Microbiol. 8 (2010), 717–729.
30. di Bernardo, D., Thompson, M.J., Gardner, T.S., Chobot, S.E., Eastwood, E.L., Wojtovich, A.P., Elliott, S.J., Schaus, S.E., Collins, J.J., Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 23 (2005), 377–383.
31. Dietterich, T.G. (2000). Ensemble methods in machine learning. G. Goos, J. Hartmanis, and J.P. van Leeuwen, eds. International Workshop on Multiple Classifier Systems, 1–15.
32. Djebali, S., Davis, C.A., Merkel, A., Dobin, A., Lassmann, T., Mortazavi, A., Tanzer, A., Lagarde, J., Lin, W., Schlesinger, F., et al. Landscape of transcription in human cells. Nature 489 (2012), 101–108.
33. Domingos, P., A few useful things to know about machine learning. Commun. ACM 55 (2012), 78–87.
34. Elowitz, M.B., Leibler, S., A synthetic oscillatory network of transcriptional regulators. Nature 403 (2000), 335–338.
35. Faith, J.J., Hayete, B., Thaden, J.T., Mogno, I., Wierzbowski, J., Cottarel, G., Kasif, S., Collins, J.J., Gardner, T.S., Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol., 5, 2007, e8.
36. Friedman, N., Inferring cellular networks using probabilistic graphical models. Science 303 (2004), 799–805.
37. Gardner, T.S., Cantor, C.R., Collins, J.J., Construction of a genetic toggle switch in Escherichia coli. Nature 403 (2000), 339–342.
38. Garnett, M.J., Edelman, E.J., Heidorn, S.J., Greenman, C.D., Dastur, A., Lau, K.W., Greninger, P., Thompson, I.R., Luo, X., Soares, J., et al. Systematic identification of genomic markers of drug sensitivity in cancer cells. Nature 483 (2012), 570–575.
39. Ghahramani, Z., Unsupervised Learning. Advanced Lectures on Machine Learning, 2004, Springer, Berlin, Heidelberg, 72–112.
40. Goh, K.-I., Cusick, M.E., Valle, D., Childs, B., Vidal, M., Barabási, A.-L., The human disease network. Proc. Natl. Acad. Sci. USA 104 (2007), 8685–8690.
41. Goodspeed, A., Heiser, L.M., Gray, J.W., Costello, J.C., Tumor-Derived Cell Lines as Molecular Models of Cancer Pharmacogenomics. Mol. Cancer Res. 14 (2016), 3–13.
42. Greene, C.S., Krishnan, A., Wong, A.K., Ricciotti, E., Zelaya, R.A., Himmelstein, D.S., Zhang, R., Hartmann, B.M., Zaslavsky, E., Sealfon, S.C., et al. Understanding multicellular function and disease with human tissue-specific networks. Nat. Genet. 47 (2015), 569–576.
43. Guyon, I., Elisseeff, A., An introduction to variable and feature selection. J. Mach. Learn. Res. 3 (2003), 1157–1182.
44. Hastie, T., Tibshirani, R., Friedman, J.H., The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2009, Springer Series in Statistics.
45. Hill, S.M., Heiser, L.M., Cokelaer, T., Unger, M., Nesser, N.K., Carlin, D.E., Zhang, Y., Sokolov, A., Paull, E.O., Wong, C.K., et al., HPN-DREAM Consortium. Inferring causal molecular networks: empirical assessment through a community-based effort. Nat. Methods 13 (2016), 310–318.
46. Hinton, G.E., Krizhevsky, A., and Wang, S.D. (2011). Transforming Auto-encoders. In Proceedings of the 21th International Conference on Artificial Neural Networks - Volume Part I. (Berlin, Heidelberg: Springer-Verlag), pp. 44–51.
47. Human Microbiome Project Consortium. The Integrative Human Microbiome Project: Dynamic Analysis of Microbiome-Host Omics Profiles during Periods of Human Health and Disease. Cell Host Microbe 16 (2014), 276–289.
48. James, G., Witten, D., Hastie, T., Tibshirani, R., An introduction to statistical learning : with applications in R. 2013, Springer, New York.
49. Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival, B. Jr., Assad-Garcia, N., Glass, J.I., Covert, M.W., A whole-cell computational model predicts phenotype from genotype. Cell 150 (2012), 389–401.
50. Karr, J.R., Phillips, N.C., Covert, M.W., WholeCellSimDB: a hybrid relational/HDF database for whole-cell model predictions. Database J. Biol. Databases Curation, 2014, 2014.
51. Khalil, A.S., Collins, J.J., Synthetic biology: applications come of age. Nat. Rev. Genet. 11 (2010), 367–379.
52. Kim, S., Thiessen, P.A., Bolton, E.E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B.A., et al. PubChem Substance and Compound databases. Nucleic Acids Res. 44:D1 (2016), D1202–D1213.
53. Krizhevsky, A., Sutskever, I., Hinton, G.E., ImageNet Classification with Deep Convolutional Neural Networks. Burges, C.J.C., Bottou, L., Weinberger, K.Q., (eds.) Advances in Neural Information Processing Systems 25, F, 2012, Curran Associates, Inc., Pereira, 1097–1105.
54. Lakkaraju, H., Bach, S.H., Leskovec, J., Interpretable Decision Sets: A Joint Framework for Description and Prediction. KDD 2016 (2016), 1675–1684.
55. Langille, M.G.I., Zaneveld, J., Caporaso, J.G., McDonald, D., Knights, D., Reyes, J.A., Clemente, J.C., Burkepile, D.E., Vega Thurber, R.L., Knight, R., et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat. Biotechnol. 31 (2013), 814–821.
56. LeCun, Y., Bengio, Y., Hinton, G., Deep learning. Nature 521 (2015), 436–444.
57. Letham, B., Rudin, C., McCormick, T.H., Madigan, D., Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model. Ann. Appl. Stat. 9 (2015), 1350–1371.
58. Leung, M.K.K., Delong, A., Alipanahi, B., Frey, B.J., Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets. Proceedings of the IEEE 104 (2016), 176–197.
59. Lipton, Z.C. (2016). The Mythos of Model Interpretability. arXiv, arXiv:1606.03490, http://arxiv.org/abs/1606.03490.
60. Little, R.J.A., Rubin, D.B., Statistical analysis with missing data. 1987, Wiley, New York.
61. Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al., GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 45 (2013), 580–585.
62. Mailman, M.D., Feolo, M., Jin, Y., Kimura, M., Tryka, K., Bagoutdinov, R., Hao, L., Kiang, A., Paschall, J., Phan, L., et al. The NCBI dbGaP database of genotypes and phenotypes. Nat. Genet. 39 (2007), 1181–1186.
63. Mall, R., Cerulo, L., Bensmail, H., Iavarone, A., Ceccarelli, M., Detection of statistically significant network changes in complex biological networks. BMC Syst. Biol., 11, 2017, 32.
64. Marbach, D., Costello, J.C., Küffner, R., Vega, N.M., Prill, R.J., Camacho, D.M., Allison, K.R., Kellis, M., Collins, J.J., Stolovitzky, G., DREAM5 Consortium. Wisdom of crowds for robust gene network inference. Nat. Methods 9 (2012), 796–804.
65. Margolin, A.A., Nemenman, I., Basso, K., Wiggins, C., Stolovitzky, G., Dalla Favera, R., Califano, A., ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics, 7(Suppl 1), 2006, S7.
66. Margolin, A.A., Bilal, E., Huang, E., Norman, T.C., Ottestad, L., Mecham, B.H., Sauerwine, B., Kellen, M.R., Mangravite, L.M., Furia, M.D., et al. Systematic analysis of challenge-driven improvements in molecular prognostic models for breast cancer. Sci. Transl. Med., 5, 2013, 181re1.
67. McCulloch, W.S., Pitts, W., A logical calculus of the ideas immanent in nervous activity. Bull. Math. Biophys. 5 (1943), 115–133.
68. Mezlini, A.M., Goldenberg, A., Incorporating networks in a probabilistic graphical model to find drivers for complex human diseases. PLoS Comput. Biol., 13, 2017, e1005580.
69. Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J., Distributed Representations of Words and Phrases and their Compositionality. Adv. Neur. Inf. Proc. Sys. 26 (2013), 3111–3119.
70. Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013b). Efficient Estimation of Word Representations in Vector Space. arXiv, arXiv:1301.3781, http://arxiv.org/abs/1301.3781.
71. Mitchell, T.M., Machine Learning. 1997, McGraw-Hill Education, New York.
72. Mukherji, S., van Oudenaarden, A., Synthetic biology: understanding biological design from synthetic circuits. Nat. Rev. Genet. 10 (2009), 859–871.
73. Musa, A., Ghoraie, L.S., Zhang, S.-D., Glazko, G., Yli-Harja, O., Dehmer, M., Haibe-Kains, B., Emmert-Streib, F., A review of connectivity map and computational approaches in pharmacogenomics. Brief. Bioinform., 18, 2017, 903.
74. Pan, S.J., Yang, Q., A Survey on Transfer Learning. IEEE Trans. Knowl. Data Eng. 22 (2010), 1345–1359.
75. Parker, D.B., Learning-Logic: Casting the Cortex of the Human Brain in Silicon. Technical Report Tr-47, 1985, MIT Press.
76. Pe'er, D., Hacohen, N., Principles and strategies for developing network models in cancer. Cell 144 (2011), 864–873.
77. Purnick, P.E.M., Weiss, R., The second wave of synthetic biology: from modules to systems. Nat. Rev. Mol. Cell Biol. 10 (2009), 410–422.
78. Rees, M.G., Seashore-Ludlow, B., Cheah, J.H., Adams, D.J., Price, E.V., Gill, S., Javaid, S., Coletti, M.E., Jones, V.L., Bodycombe, N.E., et al. Correlating chemical sensitivity and basal gene expression reveals mechanism of action. Nat. Chem. Biol. 12 (2016), 109–116.
79. Rencher, A.C., Methods of multivariate analysis. 2002, J. Wiley, New York.
80. Rokach, L., Ensemble-based classifiers. Artif. Intell. Rev. 33 (2010), 1–39.
81. Rumelhart, D.E., Hinton, G.E., Williams, R.J., Learning representations by back-propagating errors. Nature, 323, 1986, 533.
82. Sabour, S., Frosst, N., and Hinton, G.E. (2017). Dynamic Routing Between Capsules. In Proceedings of the 31st Conference on Neural Information Processing Systems, pp. 3859–3869.
83. Saeys, Y., Inza, I., Larrañaga, P., A review of feature selection techniques in bioinformatics. Bioinformatics 23 (2007), 2507–2517.
84. Saez-Rodriguez, J., Costello, J.C., Friend, S.H., Kellen, M.R., Mangravite, L., Meyer, P., Norman, T., Stolovitzky, G., Crowdsourcing biomedical research: leveraging communities as innovation engines. Nat. Rev. Genet. 17 (2016), 470–486.
85. Schadt, E.E., Lum, P.Y., Thematic review series: systems biology approaches to metabolic and cardiovascular disorders. Reverse engineering gene networks to identify key drivers of complex disease phenotypes. J. Lipid Res. 47 (2006), 2601–2613.
86. Seashore-Ludlow, B., Rees, M.G., Cheah, J.H., Cokol, M., Price, E.V., Coletti, M.E., Jones, V., Bodycombe, N.E., Soule, C.K., Gould, J., et al. Harnessing Connectivity in a Large-Scale Small-Molecule Sensitivity Dataset. Cancer Discov. 5 (2015), 1210–1223.
87. Shlens, J. (2014). A Tutorial on Principal Component Analysis. arXiv, arXiv:1404.1100, http://arxiv.org/abs/1404.1100.
88. Shoemaker, R.H., The NCI60 human tumour cell line anticancer drug screen. Nat. Rev. Cancer 6 (2006), 813–823.
89. Stark, C., Breitkreutz, B.-J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M., BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 34 (2006), D535–D539.
90. Sterling, T., Irwin, J.J., ZINC 15–Ligand Discovery for Everyone. J. Chem. Inf. Model. 55 (2015), 2324–2337.
91. Tan, M., Prediction of anti-cancer drug response by kernelized multi-task learning. Artif. Intell. Med. 73 (2016), 70–77.
92. Walsh, L.A., Alvarez, M.J., Sabio, E.Y., Reyngold, M., Makarov, V., Mukherjee, S., Lee, K.-W., Desrichard, A., Turcan, Ş., Dalin, M.G., et al. An Integrated Systems Biology Approach Identifies TRIM25 as a Key Determinant of Breast Cancer Metastasis. Cell Rep. 20 (2017), 1623–1640.
93. Werbos, P., Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. 1974, Harvard University.
94. Wishart, D.S., Knox, C., Guo, A.C., Shrivastava, S., Hassanali, M., Stothard, P., Chang, Z., Woolsey, J., DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34 (2006), D668–D672.
95. Wishart, D.S., Knox, C., Guo, A.C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., Hassanali, M., DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36 (2008), D901–D906.
96. Wolpert, D.H., Macready, W.G., No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1 (1997), 67–82.
97. Woo, J.H., Shimoni, Y., Yang, W.S., Subramaniam, P., Iyer, A., Nicoletti, P., Rodríguez Martínez, M., López, G., Mattioli, M., Realubit, R., et al. Elucidating Compound Mechanism of Action by Network Perturbation Analysis. Cell 162 (2015), 441–451.
98. Yang, W., Soares, J., Greninger, P., Edelman, E.J., Lightfoot, H., Forbes, S., Bindal, N., Beare, D., Smith, J.A., Thompson, I.R., et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41 (2013), D955–D961.
99. Yip, K.Y., Cheng, C., Gerstein, M., Machine learning and genome annotation: a match meant to be?. Genome Biol., 14, 2013, 205.
100. Zhang, L., Tan, J., Han, D., Zhu, H., From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today 22 (2017), 1680–1685.