Using Big Data to Discover Diagnostics and Therapeutics for Gastrointestinal and Liver Diseases

Gastroenterology

Volumn 152, Issue 1, 2017, Pages 53-67.e3

  Wooden, Benjamin ^a   Goossens, Nicolas ^a,b   Hoshida, Yujin ^a   Friedman, Scott L ^a  

^a MOUNT SINAI SCHOOL OF MEDICINE   (United States)

^b GENEVA UNIVERSITY HOSPITALS   (Switzerland)

Author keywords

Big Data; Drug Repurposing; Precision Medicine; Translational Bioinformatics

Indexed keywords

CLINICAL TRIAL (TOPIC); DATA ANALYSIS; DIAGNOSTIC APPROACH ROUTE; DRUG COMBINATION; DRUG REPOSITIONING; DRUG TARGETING; GASTROENTEROLOGY; GASTROINTESTINAL DISEASE; HUMAN; INFORMATION PROCESSING; INFORMATION RETRIEVAL; LIVER DISEASE; MACHINE LEARNING; MEDICAL INFORMATION SYSTEM; META ANALYSIS (TOPIC); NONHUMAN; PHARMACEUTICAL ENGINEERING; PHASE 1 CLINICAL TRIAL (TOPIC); PHASE 2 CLINICAL TRIAL (TOPIC); PRIORITY JOURNAL; RANDOMIZED CONTROLLED TRIAL (TOPIC); REVIEW; STATISTICAL ANALYSIS; SYSTEMS BIOLOGY; BIOLOGY; DATA MINING; DRUG DEVELOPMENT; GASTROINTESTINAL DISEASES; LIVER DISEASES; MOLECULARLY TARGETED THERAPY; PROCEDURES;

BIOLOGICAL MARKER;

BIOMARKERS; COMPUTATIONAL BIOLOGY; DATA MINING; DRUG DISCOVERY; GASTROINTESTINAL DISEASES; HUMANS; LIVER DISEASES; MOLECULAR TARGETED THERAPY;

EID: 85006324104     PISSN: 00165085     EISSN: 15280012     Source Type: Journal    
DOI: 10.1053/j.gastro.2016.09.065     Document Type: Review

References (132)

1. 1 Collins, F., Green, E., Guttmacher, A., et al. A vision for the future of genomics research. Nature 422 (2003), 835–847.
2. 2 Costa, F.F., Big data in biomedicine. Drug Discov Today 19 (2014), 433–440.
3. 3 Cook, C.E., Bergman, M.T., Finn, R.D., et al. The European Bioinformatics Institute in 2016: data growth and integration. Nucleic Acids Res 44 (2016), D20–D26.
4. 4 Kolesnikov, N., Hastings, E., Keays, M., et al. ArrayExpress update-simplifying data submissions. Nucleic Acids Res 43 (2015), D1113–D1116.
5. 5 Barrett, T., Wilhite, S.E., Ledoux, P., et al. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res 41 (2013), D991–D995.
6. 6 Pivovarov, R., Elhadad, N., Automated methods for the summarization of electronic health records. J Am Med Inform Assoc 22 (2015), 938–947.
7. 7 Mezghani, E., Exposito, E., Drira, K., et al. A semantic big data platform for integrating heterogeneous wearable data in healthcare. J Med Syst, 39, 2015, 185.
8. 8 Taichman, D.B., Backus, J., Baethge, C., et al. Sharing clinical trial data—a proposal from the International Committee of Medical Journal Editors. N Engl J Med 374 (2016), 384–386.
9. 9 National Cancer Institute Press Office. Newly launched Genomic Data Commons to facilitate data and clinical information sharing. 2016. Available at: http://www.cancer.gov/news-events/press-releases/2016/genomic-data-commons-launch.
10. 10 Li, L.L., Cheng, W.-Y.W., Glicksberg, B.S., et al. Identification of type 2 diabetes subgroups through topological analysis of patient similarity. Sci Transl Med 7 (2015), 1–16.
11. 11 Issa, N.T., Byers, S.W., Dakshanamurthy, S., Big data: the next frontier for innovation in therapeutics and healthcare. Expert Rev Clin Pharmacol 7 (2014), 293–298.
12. 12 Badawi, O., Brennan, T., Celi, L.A., et al. Making big data useful for health care: a summary of the inaugural MIT critical data conference. J Med Internet Res, 16, 2014, e22.
13. 13 National Research Council (US) Committee on A Framework for Developing a New Taxonomy of Disease. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. 2011, The National Academies Press, Washington, DC.
14. 14 Vanhove, W., Nys, K., Vermeire, S., Therapeutic innovations in inflammatory bowel diseases. Clin Pharmacol Ther 99 (2016), 49–58.
15. 15 Sherif, Z.A., Saeed, A., Ghavimi, S., et al. Global epidemiology of nonalcoholic fatty liver disease and perspectives on us minority populations. Dig Dis Sci 61 (2016), 1214–1225.
16. 16 Arsene, D., Farooq, O., Bataller, R., New therapeutic targets in alcoholic hepatitis. Hepatol Int 10 (2016), 538–552.
17. 17 Parkinson, D.R., McCormack, R.T., Keating, S.M., et al. Evidence of clinical utility: an unmet need in molecular diagnostics for patients with cancer. Clin Cancer Res 20 (2014), 1428–1444.
18. 18 Sawyers, C.L., van't Veer, L.J., Reliable and effective diagnostics are keys to accelerating personalized cancer medicine and transforming cancer care: a policy statement from the American Association for Cancer Research. Clin Cancer Res 20 (2014), 4978–4981.
19. 19 Poste, G., Bring on the biomarkers. Nature 469 (2011), 156–157.
20. 20 Teutsch, S.M., Bradley, L.A., Palomaki, G.E., et al. The Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Initiative: methods of the EGAPP Working Group. Genet Med 11 (2009), 3–14.
21. 21 Institute of Medicine. Evolution of translational omics: lessons learned and the path forward. 2012, The National Academies Press, Washington, DC.
22. 22 Goossens, N., Nakagawa, S., Sun, X., et al. Cancer biomarker discovery and validation. Transl Cancer Res 4 (2015), 256–269.
23. 23 Redig, A.J., Jänne, P.A., Basket trials and the evolution of clinical trial design in an era of genomic medicine. J Clin Oncol 33 (2015), 975–977.
24. 24 Gannon, H.S., Kaplan, N., Tsherniak, A., et al. Identification of an “exceptional responder” cell line to MEK1 inhibition: clinical implications for MEK-targeted therapy. Mol Cancer Res, 2015, 207–216.
25. 25 Schully, S.D., Carrick, D.M., Mechanic, L.E., et al. Leveraging biospecimen resources for discovery or validation of markers for early cancer detection. J Natl Cancer Inst, 107, 2015, djv012.
26. 26 Mello, M.M., Francer, J.K., et al. Preparing for responsible sharing of clinical trial data. N Engl J Med 369 (2013), 1651–1658.
27. 27 Jung, Y., Lee, S., Choi, H.-S., et al. Clinical validation of colorectal cancer biomarkers identified from bioinformatics analysis of public expression data. Clin Cancer Res 17 (2011), 700–709.
28. 28 Bailey, P., Chang, D.K., Nones, K., et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531 (2016), 47–52.
29. 29 Hoshida, Y., Nijman, S.M.B., Kobayashi, M., et al. Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res 69 (2009), 7385–7392.
30. 30 Guinney, J., Dienstmann, R., Wang, X., et al. The consensus molecular subtypes of colorectal cancer. Nat Med 21 (2015), 1350–1356.
31. 31 Zhang, D.Y., Goossens, N., Guo, J., et al. A hepatic stellate cell gene expression signature associated with outcomes in hepatitis C cirrhosis and hepatocellular carcinoma after curative resection. Gut 65 (2015), 1754–1764.
32. 32 Hoshida, Y., Villanueva, A., Kobayashi, M., et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N Engl J Med 359 (2008), 1995–2004.
33. 33 Hoshida, Y., Villanueva, A., Sangiovanni, A., et al. Prognostic gene expression signature for patients with hepatitis C-related early-stage cirrhosis. Gastroenterology 144 (2013), 1024–1030.
34. 34 King, L.Y., Canasto-Chibuque, C., Johnson, K.B., et al. A genomic and clinical prognostic index for hepatitis C-related early-stage cirrhosis that predicts clinical deterioration. Gut 64 (2015), 1296–1302.
35. 35 Dalerba, P., Sahoo, D., Paik, S., et al. CDX2 as a prognostic biomarker in stage II and stage III colon cancer. N Engl J Med 374 (2016), 211–222.
36. 36 Munos, B., Lessons from 60 years of pharmaceutical innovation. Nat Rev Drug Discov 8 (2009), 959–968.
37. 37 Schuhmacher, A., Gassmann, O., Hinder, M., et al. Changing R&D models in research-based pharmaceutical companies. J Transl Med, 14, 2016, 105.
38. 38 Hart, T., Xie, L., Providing data science support for systems pharmacology and its implications to drug discovery. Expert Opin Drug Discov 11 (2016), 241–256.
39. 39 Medina-Franco, J.L., Giulianotti, M.A., Welmaker, G.S., et al. Shifting from the single to the multitarget paradigm in drug discovery. Drug Discov Today 18 (2013), 495–501.
40. 40 Scannell, J.W., Blanckley, A., Boldon, H., et al. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 11 (2012), 191–200.
41. 41 Swinney, D.C., Anthony, J., How were new medicines discovered?. Nat Rev Drug Discov 10 (2011), 507–519.
42. 42 Wang, Y., Xing, J., Xu, Y., et al. In silico ADME/T modelling for rational drug design. Q Rev Biophys 48 (2015), 488–515.
43. 43 Nantasenamat, C., Prachayasittikul, V., Maximizing computational tools for successful drug discovery. Expert Opin Drug Discov 10 (2015), 321–329.
44. 44 Li, J., Zheng, S., Chen, B., et al. A survey of current trends in computational drug repositioning. Brief Bioinform 17 (2016), 2–12.
45. 45 Ashburn, T.T., Thor, K.B., Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3 (2004), 673–683.
46. 46 Power, A., Berger, A.C., Ginsburg, G.S., Genomics-enabled drug repositioning and repurposing: insights from an IOM Roundtable activity. JAMA 311 (2014), 2063–2064.
47. 47 Hurle, M.R., Yang, L., Xie, Q., et al. Computational drug repositioning: from data to therapeutics. Clin Pharmacol Ther 93 (2013), 335–341.
48. 48 Chong, C.R., Sullivan, D.J., New uses for old drugs. Nature 448 (2007), 645–646.
49. 49 Ekins, S., Williams, A.J., Krasowski, M.D., et al. In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov Today 16 (2011), 298–310.
50. 50 Kakkar, A.K., Dahiya, N., The evolving drug development landscape: from blockbusters to niche busters in the orphan drug space. Drug Dev Res 75 (2014), 231–234.
51. 51 Nair, P., Second act: drug repurposing gets a boost as academic researchers join the search for novel uses of existing drugs. Proc Natl Acad Sci U S A 110 (2013), 2430–2432.
52. 52 Collins, F.S., Varmus, H., A new initiative on precision medicine. N Engl J Med 372 (2015), 793–795.
53. 53 Lamb, J., Crawford, E.D., Peck, D., et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313 (2006), 1929–1935.
54. 54 Vidović, D., Koleti, A., Schürer, S.C., Large-scale integration of small molecule-induced genome-wide transcriptional responses, Kinome-wide binding affinities and cell-growth inhibition profiles reveal global trends characterizing systems-level drug action. Front Genet, 5, 2014, 342.
55. 55 Dudley, J.T., Sirota, M., Shenoy, M., et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med, 3, 2011, 96ra76.
56. 56 Chen, M.-H., Yang, W.-L.R., Lin, K.-T., Liu, C.-H., Liu, Y.-W., Huang, K.-W., et al. Gene expression-based chemical genomics identifies potential therapeutic drugs in hepatocellular carcinoma. PLoS One, 6, 2011, e27186.
57. 57 Wang, J., Li, M., Wang, Y., et al. Integrating subpathway analysis to identify candidate agents for hepatocellular carcinoma. Onco Targets Ther 9 (2016), 1221–1230.
58. 58 van Noort, V., Schölch, S., Iskar, M., et al. Novel drug candidates for the treatment of metastatic colorectal cancer through global inverse gene-expression profiling. Cancer Res 74 (2014), 5690–5699.
59. 59 Claerhout, S., Lim, J.Y., Choi, W., et al. Gene expression signature analysis identifies vorinostat as a candidate therapy for gastric cancer. PLoS One, 6, 2011, e24662.
60. 60 Suthram, S., Dudley, J.T., Chiang, A.P., et al. Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol, 6, 2010, e1000662.
61. 61 Nelson, M.R., Tipney, H., Painter, J.L., et al. The support of human genetic evidence for approved drug indications. Nat Genet 47 (2015), 856–860.
62. 62 Iorio, F., Knijnenburg, T.A., Vis, D.J., Bignell, G.R., Menden, M.P., et al. A landscape of pharmacogenomic interactions in cancer. Cell 166 (2016), 740–754.
63. 63 Zollanvari, A., High-dimensional statistical learning: roots, justifications, and potential machineries. Cancer Inform, 14, 2016, 109.
64. 64 Leek, J.T., Scharpf, R.B., Bravo, H.C., et al. Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11 (2010), 733–739.
65. 65 Liu, Y., Beyer, A., Aebersold, R., On the dependency of cellular protein levels on mRNA abundance. Cell 165 (2016), 535–550.
66. 66 Du, J., Bernasconi, P., Clauser, K.R., et al. Bead-based profiling of tyrosine kinase phosphorylation identifies SRC as a potential target for glioblastoma therapy. Nat Biotechnol 27 (2009), 77–83.
67. 67 Bachovchin, D.A., Koblan, L.W., Wu, W., et al. A high-throughput, multiplexed assay for superfamily-wide profiling of enzyme activity. Nat Chem Biol 10 (2014), 656–663.
68. 68 Yu, C., Mannan, A.M., Yvone, G.M., et al. High-throughput identification of genotype-specific cancer vulnerabilities in mixtures of barcoded tumor cell lines. Nat Biotechnol 34 (2016), 419–423.
69. 69 Hodos, R.A., Kidd, B.A., Shameer, K., et al. In silico methods for drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med 8 (2016), 186–210.
70. 70 Chiang, A.P., Butte, A.J., Systematic evaluation of drug-disease relationships to identify leads for novel drug uses. Clin Pharmacol Ther 86 (2009), 507–510.
71. 71 Yang, L., Agarwal, P., Systematic drug repositioning based on clinical side-effects. PLoS One, 6, 2011, e28025.
72. 72 Ye, H., Liu, Q., Wei, J., Construction of drug network based on side effects and its application for drug repositioning. PLoS One, 9, 2014, e87864.
73. 73 Zhang, P., Wang, F., Hu, J., et al. Exploring the relationship between drug side-effects and therapeutic indications. AMIA Annu Symp Proc 2013 (2013), 1568–1577.
74. 74 Kuhn, M., Letunic, I., Jensen, L.J., et al. The SIDER database of drugs and side effects. Nucleic Acids Res 44 (2016), D1075–D1079.
75. 75 Xu, R., Li, L.L., Wang, Q., Towards building a disease-phenotype knowledge base: extracting disease-manifestation relationship from literature. Bioinformatics 29 (2013), 2186–2194.
76. 76 Gottlieb, A., Stein, G.Y., Ruppin, E., et al. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Mol Syst Biol, 7, 2014, 496.
77. 77 Zhang, P., Wang, F., Hu, J., Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity. AMIA Annu Symp Proc 2014 (2014), 1258–1267.
78. 78 Cheng, L., Li, J., Ju, P., et al. SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association. PLoS One, 9, 2014, e99415.
79. 79 Wang, W., Yang, S., Zhang, X., et al. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics 30 (2014), 1–8.
80. 80 Iwata, H., Sawada, R., Mizutani, S., et al. Systematic drug repositioning for a wide range of diseases with integrative analyses of phenotypic and molecular data. J Chem Inf Model 55 (2015), 446–459.
81. 81 Chan, S.Y., Loscalzo, J., The emerging paradigm of network medicine in the study of human disease. Circ Res 111 (2012), 359–374.
82. 82 Hopkins, A.L., Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4 (2008), 682–690.
83. 83 Von Eichborn, J., Murgueitio, M.S., Dunkel, M., et al. PROMISCUOUS: a database for network-based drug-repositioning. Nucleic Acids Res 39 (2011), D1060–D1066.
84. 84 Lee, H., Bae, T., Lee, J.-H., et al. Rational drug repositioning guided by an integrated pharmacological network of protein, disease and drug. BMC Syst Biol, 6, 2012, 80.
85. 85 Iorio, F., Shrestha, R.L., Levin, N., et al. A semi-supervised approach for refining transcriptional signatures of drug response and repositioning predictions. PLoS One, 10, 2015, e0139446.
86. 86 Menche, J., Sharma, A., Kitsak, M., et al. Uncovering disease-disease relationships through the incomplete interactome. Science, 347, 2015, 1257601.
87. 87 Swanson, D.R., Medical literature as a potential source of new knowledge. Bull Med Libr Assoc 78 (1990), 29–37.
88. 88 Cohen, T., Widdows, D., Stephan, C., et al. Predicting high-throughput screening results with scalable literature-based discovery methods. CPT Pharmacometrics Syst Pharmacol, 3, 2014, e140.
89. 89 Tari, L., Vo, N., Liang, S., et al. Identifying novel drug indications through automated reasoning. PLoS One, 7, 2012, e40946.
90. 90 Gramatica, R., Di Matteo, T., Giorgetti, S., et al. Graph theory enables drug repurposing—how a mathematical model can drive the discovery of hidden mechanisms of action. PLoS One, 9, 2014, e84912.
91. 91 Liu, Y., Liang, Y., Wishart, D., PolySearch2: a significantly improved text-mining system for discovering associations between human diseases, genes, drugs, metabolites, toxins and more. Nucleic Acids Res 43 (2015), W535–W542.
92. 92 Ma, D.L., Chan, D.S.H., Leung, C.H., Drug repositioning by structure-based virtual screening. Chem Soc Rev 42 (2013), 2130–2141.
93. 93 Lee, A., Lee, K., Kim, D., Using reverse docking for target identification and its applications for drug discovery. Expert Opin Drug Discov 11 (2016), 707–715.
94. 94 Li, H., Gao, Z., Kang, L., et al. TarFisDock: a web server for identifying drug targets with docking approach. Nucleic Acids Res 34 (2006), 219–224.
95. 95 Douguet, D., e-LEA3D: a computational-aided drug design web server. Nucleic Acids Res 38 (2010), W615–W621.
96. 96 Liu, X., Ouyang, S., Yu, B., et al. PharmMapper server: a web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res 38 (2010), W609–W614.
97. 97 Wang, X., Chen, H., Yang, F., et al. IDrug: a web-accessible and interactive drug discovery and design platform. J Cheminform 6 (2014), 1–8.
98. 98 Luo, H., Chen, J., Shi, L., et al. DRAR-CPI: a server for identifying drug repositioning potential and adverse drug reactions via the chemical-protein interactome. Nucleic Acids Res 39 (2011), W492–W498.
99. 99 Keiser, M.J., Setola, V., Irwin, J.J., et al. Predicting new molecular targets for known drugs. Nature 462 (2009), 175–181.
100. 100 Wang, L., Ma, C., Wipf, P., et al. TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. AAPS J 15 (2013), 395–406.
101. 101 Nantasenamat, C., Isarankura-Na-Ayudhya, C., Naenna, T., et al. A practical overview of quantitative structure-activity relationship. EXCLI J 8 (2009), 74–88.
102. 102 Nantasenamat, C., Worachartcheewan, A., Jamsak, S., et al. AutoWeka: toward an automated data mining software for QSAR and QSPR Studies. Cartwright, H., (eds.) Artificial neural networks, 2nd ed., 2015, Springer Science+Business Media, New York, 119–147.
103. 103 Cheng, F., Li, W., Zhou, Y., et al. AdmetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 52 (2012), 3099–3105.
104. 104 Pires, D.E.V., Blundell, T.L., Ascher, D.B., pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58 (2015), 4066–4072.
105. 105 García-Domenech, R., Gálvez-Llompart, M., Zanni, R., et al. QSAR methods for the discovery of new inflammatory bowel disease drugs. Expert Opin Drug Discov 8 (2013), 933–949.
106. 106 Gálvez-Llompart, M., Recio, M.C., García-Domenech, R., Topological virtual screening: a way to find new compounds active in ulcerative colitis by inhibiting NF-κB. Mol Divers 15 (2011), 917–924.
107. 107 Yang, J.S., Chun, K., Park, J.E., et al. Structure based optimization of chromen-based TNF-α converting enzyme (TACE) inhibitors on S1’ pocket and their quantitative structure-activity relationship (QSAR) study. Bioorg Med Chem 18 (2010), 8618–8629.
108. 108 Huang, H., Zhang, P., Qu, X.A., et al. Systematic prediction of drug combinations based on clinical side-effects. Sci Rep, 4, 2014, 7160.
109. 109 Lee, J.-H., Kim, D.G., Bae, T.J., et al. CDA: combinatorial drug discovery using transcriptional response modules. PLoS One, 7, 2012, e42573.
110. 110 Prahallad, A., Bernards, R., Opportunities and challenges provided by crosstalk between signalling pathways in cancer. Oncogene 35 (2015), 1–7.
111. 111 Johannessen, C.M., Johnson, L.A., Piccioni, F., et al. A melanocyte lineage program confers resistance to MAP kinase pathway inhibition. Nature 504 (2013), 138–142.
112. 112 Sun, X., Vilar, S., Tatonetti, N.P., High-throughput methods for combinatorial drug discovery. Sci Transl Med, 5, 2013, 205rv1.
113. 113 Chen, L., Li, B.-Q., Zheng, M.-Y., et al. Prediction of effective drug combinations by chemical interaction, protein interaction and target enrichment of KEGG pathways. Biomed Res Int, 2013, 2013, 723780.
114. 114 Huang, L., Li, F., Sheng, J., et al. DrugComboRanker: drug combination discovery based on target network analysis. Bioinformatics 30 (2014), i228–i236.
115. 115 Liu, Y., Wei, Q., Yu, G., et al. DCDB 2.0: a major update of the drug combination database. Database (Oxford), 2014, 2014, bau124.
116. 116 Lima, A.N., Philot, E.A., Trossini, G.H.G., et al. Use of machine learning approaches for novel drug discovery. Expert Opin Drug Discov 11 (2016), 225–239.
117. 117 Smith, T.C., Frank, E., Introducing machine learning concepts with WEKA. Methods Mol Biol 1418 (2016), 353–378.
118. 118 Napolitano, F., Zhao, Y., Moreira, V.M., et al. Drug repositioning: a machine-learning approach through data integration. J Cheminform, 5, 2013, 30.
119. 119 Mamoshina, P., Vieira, A., Putin, E., et al. Applications of deep learning in biomedicine. Mol Pharm 13 (2016), 1445–1454.
120. 120 Aliper, A., Plis, S., Artemov, A., et al. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol Pharm 13 (2016), 2524–2530.
121. 121 Xu, Y., Dai, Z., Chen, F., et al. Deep learning for drug-induced liver injury. J Chem Inf Model 55 (2015), 2085–2093.
122. 122 Zhou, Z.-H., Ensemble learning. Li, S., (eds.) Encyclopedia of biometrics, 2009, Springer Science+Business Media, New York, NY, 270–273.
123. 123 Wan, Q., Pal, R., An ensemble based top performing approach for NCI-DREAM drug sensitivity prediction challenge. PLoS One, 9, 2014, e101183.
124. 124 Reshef, D.N., Reshef, Y.A., Finucane, H.K., et al. Detecting novel associations in large data sets. Science 334 (2011), 1518–1524.
125. 125 Agarwal, P., Owzar, K., Next generation distributed computing for cancer research. Cancer Inform 14 (2015), 1–13.
126. 126 Good, B.M., Su, A.I., Crowdsourcing for bioinformatics. Bioinformatics 29 (2013), 1925–1933.
127. 127 Jayaraj, P.B., Ajay, M.K., Nufail, M., et al. GPURFSCREEN: a GPU based virtual screening tool using random forest classifier. J Cheminform, 8, 2016, 12.
128. 128 Alyass, A., Turcotte, M., Meyre, D., From big data analysis to personalized medicine for all: challenges and opportunities. BMC Med Genomics, 8, 2015, 33.
129. 129 Bierer, B.E., Li, R., Barnes, M., et al. A global, neutral platform for sharing trial data. N Engl J Med 374 (2016), 2411–2413.
130. 130 Kim, J.H., Sohn, B.H., Lee, H.S., et al. Genomic predictors for recurrence patterns of hepatocellular carcinoma: model derivation and validation. PLoS Med, 11, 2014, e1001770.
131. 131 Ji, J., Eggert, T., Budhu, A., et al. Hepatic stellate cell and monocyte interaction contributes to poor prognosis in hepatocellular carcinoma. Hepatology 62 (2015), 481–495.
132. 132 Huang, H., Shiffman, M.L., Friedman, S., et al. A 7 gene signature identifies the risk of developing cirrhosis in patients with chronic hepatitis C. Hepatology 46 (2007), 297–306.