Integrative methods for analyzing big data in precision medicine

Proteomics

Volumn 16, Issue 5, 2016, Pages 741-758

  Gligorijević, Vladimir ^a   Malod Dognin, Noël ^a   Pržulj, Nataša ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Big data; Bioinformatics; Integration methods; Personalized medicine

Indexed keywords

BIOLOGICAL MARKER; TRANSCRIPTOME;

DATA ANALYSIS; DRUG REPOSITIONING; EPIGENETICS; EXOMICS; EXPOSOMICS; FLUXOMICS; GENOMICS; GLYCOBIOLOGY; HUMAN; INTERACTOMICS; MEDICAL INFORMATICS; METABOLOMICS; METAGENOMICS; PERSONALIZED MEDICINE; PHENOMICS; PRIORITY JOURNAL; PROTEOMICS; REVIEW; TRANSCRIPTOMICS; BIOLOGY; GENETICS; MEDICAL RESEARCH; PROCEDURES;

BIOMARKERS; BIOMEDICAL RESEARCH; COMPUTATIONAL BIOLOGY; DRUG REPOSITIONING; EPIGENOMICS; HUMANS; MEDICAL INFORMATICS; METABOLOMICS; PRECISION MEDICINE; PROTEOMICS; TRANSCRIPTOME;

EID: 84959496982     PISSN: 16159853     EISSN: 16159861     Source Type: Journal    
DOI: 10.1002/pmic.201500396     Document Type: Review

References (177)

1. Hood, L., Friend, S. H., Predictive, personalized, preventive, participatory (p4) cancer medicine. Nat. Rev. Clin. Oncol. 2011, 8, 184-187.
2. Mirnezami, R., Nicholson, J., Darzi, A., Preparing for precision medicine. N. Engl. J. Med. 2012, 366, 489-491.
3. Table of blood group systems v4.0. International Society of Blood Transfusion, Amsterdam 2014.
4. Smits, J., De Meester, J., Persijn, G., Claas, F., Vanrenterghem, Y., Long-term results of solid organ transplantation. Report from the eurotransplant international foundation. Clin. Transplant. 1995, 109-127.
5. Takemoto, S., Terasaki, P. I., Cecka, J. M., Cho, Y. W., Gjertson, D. W., Survival of nationally shared, hla-matched kidney transplants from cadaveric donors. N. Engl. J. Med. 1992, 327, 834-839.
6. Thorogood, J., Persijn, G. G., Schreuder, G. M., Zantvoort, F. A. et al., The effect of hla matching on kidney graft survival in separate post-transplantation intervals. Transplantation 1990, 50, 146-149.
7. Mitsuishi, Y., Terasaki, P., HLA matching effect on five-year graft survival and half-life in the cyclosporine era. Kidney Int. 1992, Suppl 38, S172-S175.
8. Beyer, M. A., Laney, D., The Importance of 'Big Data': A Definition, Gartner, Stamford, CT 2012.
9. International Human Genome Sequencing Consortium, I. H. G. S. et al., Finishing the euchromatic sequence of the human genome. Nature 2004, 431, 931-945.
10. McDermott, J. E., Wang, J., Mitchell, H., Webb-Robertson, B. J. et al., Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data. Expert Opin. Med. Diagn. 2013, 7, 37-51.
11. Kato, H., Takahashi, S., Saito, K., Omics and integrated omics for the promotion of food and nutrition science. J. Tradit. Complement. Med. 2011, 1, 25-30.
12. Cho, I., Blaser, M. J., The human microbiome: at the interface of health and disease. Nat. Rev. Genet. 2012, 13, 260-270.
13. Yuan, Y., Failmezger, H., Rueda, O. M., Ali, H. R. et al., Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling. Sci. Transl. Med. 2012, 4, 143-157.
14. Kumar, V., Gu, Y., Basu, S., Berglund, A. et al., Radiomics: the process and the challenges. Magn. Reson. Imaging 2012, 30, 1234-1248.
15. Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S. et al., Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 2012, 48, 441-446.
16. Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M., Keller, P. J., Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 2013, 10, 413-420.
17. Mardis, E. R., The $1, 000 genome, the $100, 000 analysis. Genome Med. 2010, 2, 84-86.
18. Margolis, R., Derr, L., Dunn, M., Huerta, M. et al., The national institutes of health's big data to knowledge (bd2k) initiative: capitalizing on biomedical big data. J. Am. Med. Inform. Assoc. 2014, 21, 957-958.
19. Lötvall, J., Akdis, C. A., Bacharier, L. B., Bjermer, L. et al., Asthma endotypes: a new approach to classification of disease entities within the asthma syndrome. J. Allergy Clin. Immunol. 2011, 127, 355-360.
20. Boland, M. R., Hripcsak, G., Shen, Y., Chung, W. K., Weng, C., Defining a comprehensive verotype using electronic health records for personalized medicine. J. Am. Med. Inform. Assoc. 2013, 20, e232-e238.
21. Schulam, P., Wigley, F., Saria, S., Clustering longitudinal clinical marker trajectories from electronic health data: applications to phenotyping and endotype discovery. In Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, Texas, USA 2015, pp. 2956-2964.
22. Verhaak, R. G., Hoadley, K. A., Purdom, E., Wang, V. et al., Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in pdgfra, idh1, egfr, and nf1. Cancer Cell 2010, 17, 98-110.
23. Lewis, S., Foltynie, T., Blackwell, A. D., Robbins, T. W. et al., Heterogeneity of Parkinson's disease in the early clinical stages using a data driven approach. J. Neurol. Neurosurg. Psychiatry 2005, 76, 343-348.
24. Gutman, S., Kessler, L. G., The us food and drug administration perspective on cancer biomarker development. Nat. Rev. Cancer 2006, 6, 565-571.
25. Davis, J. C., Furstenthal, L., Desai, A. A., Norris, T. et al., The microeconomics of personalized medicine: today's challenge and tomorrow's promise. Nat. Rev. Drug Discov. 2009, 8, 279-286.
26. Ashburn, T. T., Thor, K. B., Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 2004, 3, 673-683.
27. Wheeler, D. A., Srinivasan, M., Egholm, M., Shen, Y. et al., The complete genome of an individual by massively parallel dna sequencing. Nature 2008, 452, 872-876.
28. Ng, S. B., Turner, E. H., Robertson, P. D., Flygare, S. D. et al., Targeted capture and massively parallel sequencing of 12 human exomes. Nature 2009, 461, 272-276.
29. Choi, M., Scholl, U. I., Ji, W., Liu, T. et al., Genetic diagnosis by whole exome capture and massively parallel dna sequencing. Proc. Natl. Acad. Sci. USA 2009, 106, 19096-19101.
30. Owens, B., The single life. Nature 2012, 491, 27-29.
31. Shapiro, E., Biezuner, T., Linnarsson, S., Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 2013, 14, 618-630.
32. Lupski, J. R., Genome mosaicism-one human, multiple genomes. Science 2013, 341, 358-359.
33. Ziller, M. J., Gu, H., Müller, F., Donaghey, J. et al., Charting a dynamic dna methylation landscape of the human genome. Nature 2013, 500, 477-481.
34. Rivera, C. M., Ren, B., Mapping human epigenomes. Cell 2013, 155, 39-55.
35. Dawson, M. A., Kouzarides, T., Cancer epigenetics: from mechanism to therapy. Cell 2012, 150, 12-27.
36. Suvà, M. L., Riggi, N., Bernstein, B. E., Epigenetic reprogramming in cancer. Science 2013, 339, 1567-1570.
37. Vishwanathan, N., Le, H., Le, T., Hu, W.-S., Advancing biopharmaceutical process science through transcriptome analysis. Curr. Opin. Biotechnol. 2014, 30, 113-119.
38. Marian, A., Sequencing your genome: what does it mean? Methodist DeBakey Cardiovasc. J. 2014, 10, 3-6.
39. Allocco, D. J., Kohane, I. S., Butte, A. J., Quantifying the relationship between co-expression, co-regulation and gene function. BMC Bioinformatics 2004, 5, 18.
40. DeRisi, J., Penland, L., Brown, P. O., Bittner, M. L. et al., Use of a cdna microarray to analyse gene expression patterns in human cancer. Nat. Genet. 1996, 14, 457-460.
41. Volinia, S., Calin, G. A., Liu, C. G., Ambs, S. et al., A microrna expression signature of human solid tumors defines cancer gene targets. Proc. Natl. Acad. Sci. USA 2006, 103, 2257-2261.
42. Van't Veer, L.J., Dai, H., Van De Vijver, M. J., He, Y. D. et al., Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002, 415, 530-536.
43. Jensen, O. N., Modification-specific proteomics: characterization of post-translational modifications by mass spectrometry. Curr. Opin. Chem. Biol. 2004, 8, 33-41.
44. Ong, S.-E., Mann, M., Mass spectrometry-based proteomics turns quantitative. Nat. Chem. Biol. 2005, 1, 252-262.
45. Fields, S., Song, O. K., A novel genetic system to detect protein-protein interactions. Nature 1989, 340, 245-246.
46. Ho, Y., Gruhler, A., Heilbut, A., Bader, G. D. et al., Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002, 415, 180-183.
47. Kelley, B. P., Sharan, R., Karp, R. M., Sittler, T. et al., Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc. Natl. Acad. Sci. USA 2003, 100, 11394-11399.
48. Kuchaiev, O., Milenković, T., Memišević, V., Hayes, W., Pržulj, N., Topological network alignment uncovers biological function and phylogeny. J. R. Soc. Interface 2010, 7, 1341-1354.
49. Bandyopadhyay, S., Sharan, R., Ideker, T., Systematic identification of functional orthologs based on protein network comparison. Genome Res. 2006, 16, 428-435.
50. Daviss, B., Growing pains for metabolomics. The Scientist 2005, 19, 25-28.
51. Lowe, J. B., Marth, J. D., A genetic approach to mammalian glycan function. Annu. Rev. Biochem. 2003, 72, 643-691.
52. Kinjo, Y., Wu, D., Kim, G., Xing, G. W. et al., Recognition of bacterial glycosphingolipids by natural killer t cells. Nature 2005, 434, 520-525.
53. Crocker, P. R., Siglecs: sialic-acid-binding immunoglobulin-like lectins in cell-cell interactions and signalling. Curr. Opin. Struct. Biol. 2002, 12, 609-615.
54. Sasisekharan, R., Shriver, Z., Venkataraman, G., Narayanasami, U., Roles of heparan-sulphate glycosaminoglycans in cancer. Nat. Rev. Cancer 2002, 2, 521-528.
55. Fry, E. E., Lea, S. M., Jackson, T., Newman, J. W. et al., The structure and function of a foot-and-mouth disease virus-oligosaccharide receptor complex. EMBO J. 1999, 18, 543-554.
56. Winter, G., Krömer, J. O., Fluxomics-connecting 'omics analysis and phenotypes. Environ. Microbiol. 2013, 15, 1901-1916.
57. Visscher, P. M., Brown, M. A., McCarthy, M. I., Yang, J., Five years of GWAS discovery. Am. J. Hum. Genet. 2012, 90, 7-24.
58. Viaud, S., Saccheri, F., Mignot, G., Yamazaki, T. et al., The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 2013, 342, 971-976.
59. Rajendhran, J., Gunasekaran, P., Human microbiomics. Indian J. Microbiol. 2010, 50, 109-112.
60. 1000 Genomes Project Consortium, An integrated map of genetic variation from 1, 092 human genomes. Nature 2012, 491, 56-65.
61. Ajay, S. S., Parker, S. C., Abaan, H. O., Fajardo, K. V. F., Margulies, E. H., Accurate and comprehensive sequencing of personal genomes. Genome Res. 2011, 21, 1498-1505.
62. Kalisky, T., Quake, S. R., Single-cell genomics. Nat. Methods 2011, 8, 311-314.
63. Fan, H. C., Gu, W., Wang, J., Blumenfeld, Y. J. et al., Non-invasive prenatal measurement of the fetal genome. Nature 2012, 487, 320-324.
64. Chen, Y.-C., Rajagopala, S. V., Stellberger, T., Uetz, P., Exhaustive benchmarking of the yeast two-hybrid system. Nat. Methods 2010, 7, 667-668.
65. Jagadish, H. V., Gehrke, J., Labrinidis, A., Papakonstantinou, Y. et al., Big data and its technical challenges. Commun. ACM 2014, 57, 86-94.
66. Brown, G. R., Hem, V., Katz, K. S., Ovetsky, M. et al., Gene: a gene-centered information resource at NCBI. Nucleic Acids Res. 2015, 43, D36-D42.
67. Dimmer, E. C., Huntley, R. P., Alam-Faruque, Y., Sawford, T. et al., The uniprot-go annotation database in 2011. Nucleic Acids Res. 2012, 40, D565-D570.
68. ENCODE Project Consortium, The encode (encyclopedia of DNA elements) project. Science 2004, 306, 636-640.
69. Bernstein, B. E., Stamatoyannopoulos, J. A., Costello, J. F., Ren, B. et al., The NIH roadmap epigenomics mapping consortium. Nat. Biotechnol. 2010, 28, 1045-1048.
70. Teng, L., He, B., Wang, J., Tan, K., 4Dgenome: a comprehensive database of chromatin interactions. Bioinformatics 2015, 31, 2560-2564.
71. Xin, Y., Chanrion, B., O'Donnell, A. H., Milekic, M. et al., MethylomeDB: a database of DNA methylation profiles of the brain. Nucleic Acids Res. 2012, 40, D1245-D1249.
72. Edgar, R., Domrachev, M., Lash, A. E., Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002, 30, 207-210.
73. Petryszak, R., Burdett, T., Fiorelli, B., Fonseca, N. A. et al., Expression atlas update-a database of gene and transcript expression from microarray-and sequencing-based functional genomics experiments. Nucleic Acids Res. 2014, 42, D926-D932.
74. Lamb, J., Crawford, E. D., Peck, D., Modell, J. W. et al., The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006, 313, 1929-1935.
75. Okamura, Y., Aoki, Y., Obayashi, T., Tadaka, S. et al., CoxpresDB in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems. Nucleic Acids Res. 2014, 43, D82-D86.
76. van Dam, S., Craig, T., de Magalhães, J. P., Genefriends: a human RNA-seq-based gene and transcript co-expression database. Nucleic Acids Res. 2015, 43, D1124-D1132.
77. UniProt Consortium, Uniprot: a hub for protein information. Nucleic Acids Res. 2014, 43, D204-D212.
78. Gaudet, P., Michel, P. A., Zahn-Zabal, M., Cusin, I. et al., The nextprot knowledgebase on human proteins: current status. Nucleic Acids Res. 2015, 43, D764-D770.
79. Berman, H. M., Westbrook, J., Feng, Z., Gilliland, G. et al., The protein data bank. Nucleic Acids Res. 2000, 28, 235-242.
80. Uhlén, M., Fagerberg, L., Hallström, B. M., Lindskog, C. et al., Tissue-based map of the human proteome. Science 2015, 347, 1260419.
81. Kerrien, S., Aranda, B., Breuza, L., Bridge, A. et al., The intact molecular interaction database in 2012. Nucleic Acids Res. 2011, 40, D841-D846.
82. Chatr-aryamontri, A., Breitkreutz, B. J., Oughtred, R., Boucher, L. et al., The BioGRID interaction database: 2015 update. Nucleic Acids Res. 2014, 43, D470-D478.
83. Brown, K. R., Jurisica, I., Online predicted human interaction database. Bioinformatics 2005, 21, 2076-2082.
84. Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K. et al., String v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2014, 43, D447-D452.
85. Wishart, D. S., Jewison, T., Guo, A. C., Wilson, M. et al., HMDB 3.0-the human metabolome database in 2013. Nucleic Acids Res. 2012, 41, D801-D807.
86. Ogata, H., Goto, S., Sato, K., Fujibuchi, W. et al., KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 1999, 27, 29-34.
87. Jewison, T., Su, Y., Disfany, F. M., Liang, Y. et al., Smpdb 2.0: big improvements to the small molecule pathway database. Nucleic Acids Res. 2014, 42, D478-D484.
88. Croft, D., Mundo, A. F., Haw, R., Milacic, M. et al., The REACTOME pathway knowledgebase. Nucleic Acids Res. 2014, 42, D472-D477.
89. Shakhsheer, B., Anderson, M., Khatib, K., Tadoori, L. et al., Sugarbind database (sugarbinddb): a resource of pathogen lectins and corresponding glycan targets. J. Mol. Recognit. 2013, 26, 426-431.
90. Campbell, M. P., Peterson, R., Mariethoz, J., Gasteiger, E. et al., UnicarbKB: building a knowledge platform for glycoproteomics. Nucleic Acids Res. 2013, 42, D215-D221.
91. Hashimoto, K., Goto, S., Kawano, S., Aoki-Kinoshita, K. F. et al., KEGG as a glycome informatics resource. Glycobiology 2006, 16, 63R-70R.
92. Hamosh, A., Scott, A. F., Amberger, J. S., Bocchini, C. A., McKusick, V. A., Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005, 33, D514-D517.
93. Mailman, M. D., Feolo, M., Jin, Y., Kimura, M. et al., The NCBI dbgap database of genotypes and phenotypes. Nat. Genet. 2007, 39, 1181-1186.
94. Welter, D., MacArthur, J., Morales, J., Burdett, T. et al., The nhgri GWAS catalog, a curated resource of snp-trait associations. Nucleic Acids Res. 2014, 42, D1001-D1006.
95. Forbes, S. A., Beare, D., Gunasekaran, P., Leung, K. et al., Cosmic: exploring the world's knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2015, 43, D805-D811.
96. Weinstein, J. N., Collisson, E. A., Mills, G. B., Shaw, K. R. M. et al., The cancer genome atlas pan-cancer analysis project. Nat. Genet. 2013, 45, 1113-1120.
97. Law, V., Knox, C., Djoumbou, Y., Jewison, T. et al., Drugbank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2014, 42, D1091-D1097.
98. Wang, Y., Xiao, J., Suzek, T. O., Zhang, J. et al., PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009, 37, W623-W633.
99. Lim, E., Pon, A., Djoumbou, Y., Knox, C. et al., T3DB: a comprehensively annotated database of common toxins and their targets. Nucleic Acids Res. 2010, 38, D781-D786.
100. Scalbert, A., Andres-Lacueva, C., Arita, M., Kroon, P. et al., Databases on food phytochemicals and their health-promoting effects. J. Agric. Food Chem. 2011, 59, 4331-4348.
101. Brown, J. A., Rudie, J. D., Bandrowski, A., Van Horn, J. D., Bookheimer, S. Y., The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis. Front. Neuroinform. 2012, 6.
102. Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. et al., The WU-Minn human connectome project: an overview. Neuroimage 2013, 80, 62-79.
103. Human Microbiome Project Consortium, Structure, function and diversity of the healthy human microbiome. Nature 2012, 486, 207-214.
104. Greene, C. S., Tan, J., Ung, M., Moore, J. H., Cheng, C., Big data bioinformatics. J. Cell. Physiol. 2014, 229, 1896-1900.
105. Gligoriević, V., Pržulj, N., Methods for biological data integration: perspectives and challenges. J. R. Soc. Interface 2015, 12, 20150571.
106. Vapnik, V. N., Vapnik, V., Statistical Learning Theory, Vol. 1, Wiley, New York 1998.
107. Scholkopf, B., Smola, A. J., Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, MA, USA 2001.
108. Freedman, D. A., Statistical Models: Theory and Practice, Cambridge University Press, New York, USA 2009.
109. Hartigan, J. A., Clustering Algorithms (99th Edn.), John Wiley & Sons, Inc., New York, NY, USA 1975.
110. Lee, D. D., Seung, H. S., Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401, 788-791.
111. Wang, F., Li, T., Zhang, C., Semi-supervised clustering via matrix factorization, in: SDM, SIAM, Atlanta, Georgia, USA 2008, 1-12.
112. Saria, S., Goldenberg, A., Subtyping: what it is and its role in precision medicine. Intell. Syst. IEEE 2015, 30, 70-75.
113. Network, C. G. A., Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012, 487, 330-337.
114. Cancer Genome Atlas Network, Comprehensive molecular portraits of human breast tumours. Nature 2012, 490, 61-70.
115. Cancer Genome Atlas Research Network, Integrated genomic analyses of ovarian carcinoma. Nature 2011, 474, 609-615.
116. Wang, L., Li, F., Sheng, J., Wong, S. T., A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles. BMC Genomics 2015, 16, S6.
117. de Souto, M. C., Costa, I. G., de Araujo, D. S., Ludermir, T. B., Schliep, A., Clustering cancer gene expression data: a comparative study. BMC Bioinformatics 2008, 9, 497.
118. Monti, S., Tamayo, P., Mesirov, J., Golub, T., Consensus clustering: a resampling-based method for class discovery and visualization of gene expression microarray data. Mach. Learn. 2003, 52, 91-118.
119. Brunet, J.-P., Tamayo, P., Golub, T. R., Mesirov, J. P. Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl. Acad. Sci. USA 2004, 101, 4164-4169.
120. Alcaraz, N., Pauling, J., Batra, R., Barbosa, E. et al., Keypathwayminer 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with cytoscape. BMC Syst. Biol. 2014, 8, 99.
121. Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D. et al., Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using paradigm. Bioinformatics 2010, 26, i237-i245.
122. List, M., Hauschild, A. C., Tan, Q., Kruse, T. A. et al., Classification of breast cancer subtypes by combining gene expression and dna methylation data. J. Integr. Bioinform. 2014, 11, 236.
123. Kristensen, V. N., Lingjærde, O. C., Russnes, H. G., Vollan, H. K. M. et al., Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer 2014, 14, 299-313.
124. Shen, R., Olshen, A. B., Ladanyi, M., Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 2009, 25, 2906-2912.
125. Curtis, C., Shah, S. P., Chin, S. F., Turashvili, G. et al., The genomic and transcriptomic architecture of 2, 000 breast tumours reveals novel subgroups. Nature 2012, 486, 346-352.
126. Shen, R., Mo, Q., Schultz, N., Seshan, V. E. et al., Integrative subtype discovery in glioblastoma using icluster. PLoS One 2012, 7, e35236.
127. Yuan, Y., Savage, R. S., Markowetz, F., Patient-specific data fusion defines prognostic cancer subtypes. PLoS Comput. Biol. 2011, 7, e1002227.
128. Ben-Gal, I., Bayesian Networks, Chapter 1, John Wiley & Sons, Ltd, Hoboken, New Jersey, USA 2008.
129. Ray, P., Zheng, L., Lucas, J., Carin, L., Bayesian joint analysis of heterogeneous genomics data. Bioinformatics 2014, 30, 1370-1376.
130. Lock, E. F., Hoadley, K. A., Marron, J., Nobel, A. B., Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. Ann. Appl. Stat. 2013, 7, 523-542.
131. Wang, B., Mezlini, A. M., Demir, F., Fiume, M. et al., Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 2014, 11, 333-337.
132. Ng, A. Y., Jordan, M. I., Weiss, Y., On spectral clustering: analysis and an algorithm. Adv. Neural Inf. Process. Syst. 2002, 2, 849-856.
133. Hofree, M., Shen, J. P., Carter, H., Gross, A., Ideker, T., Network-based stratification of tumor mutations. Nat. Methods 2013, 10, 1108-1115.
134. Vogelstein, B., Papadopoulos, N., Velculescu, V. E., Zhou, S. et al., Cancer genome landscapes. Science 2013, 339, 1546-1558.
135. Cai, D., He, X., Wu, X., Han, J., Non-negative matrix factorization on manifold. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on. IEEE, Pisa, Italy 2008, pp. 63-72.
136. Gligorijević, V., Malod-Dognin, N., Pržulj, N., Patient-specific data fusion for cancer stratification and personalised treatment. Pac. Symp. Biocomput. 2015, 321-332.
137. Mo, Q., Wang, S., Seshan, V. E., Olshen, A. B. et al., Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc. Natl. Acad. Sci. USA 2013, 110, 4245-4250.
138. Napolitano, F., Zhao, Y., Moreira, V. M., Tagliaferri, R. et al., Drug repositioning: a machine-learning approach through data integration. J. Cheminformatics 2013, 5, 30-39.
139. Wang, Y., Chen, S., Deng, N., Wang, Y., Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS One 2013, 8, e78518.
140. Zheng, X., Ding, H., Mamitsuka, H., Zhu, S., Collaborative matrix factorization with multiple similarities for predicting drug-target interactions.In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining ACM 2013, pp. 1025-1033.
141. Zhang, P., Wang, F., Hu, J., Towards drug repositioning: a unified computational framework for integrating multiple aspects of drug similarity and disease similarity. In AMIA Annual Symposium Proceedings, Vol. 2014 American Medical Informatics Association 2014, p. 1258.
142. Gottlieb, A., Stein, G. Y., Ruppin, E., Sharan, R., Predict: a method for inferring novel drug indications with application to personalized medicine. Mol. Syst. Biol. 2011, 7, 496.
143. Huang, Y.-F., Yeh, H.-Y., Soo, V.-W., Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation. BMC Med. Genomics 2013, 6, 1-14.
144. Daminelli, S., Haupt, V. J., Reimann, M., Schroeder, M., Drug repositioning through incomplete bi-cliques in an integrated drug-target-disease network. Integr. Biol. 2012, 4, 778-788.
145. Jiang, W., Chen, X., Liao, M., Li, W. et al., Identification of links between small molecules and miRNAs in human cancers based on transcriptional responses. Sci. Rep. 2012, 2, 1-9.
146. Chung, S., Nakagawa, H., Uemura, M., Piao, L. et al., Association of a novel long non-coding rna in 8q24 with prostate cancer susceptibility. Cancer Sci. 2011, 102, 245-252.
147. Biswas, A. K., Kang, M., Kim, D. C., Ding, C. H. et al. Inferring disease associations of the long non-coding RNAs through non-negative matrix factorization. Netw. Model. Anal. Health Inform. Bioinform. 2015, 4, 1-8.
148. Dudley, J. T., Deshpande, T., Butte, A. J., Exploiting drug-disease relationships for computational drug repositioning. Brief. Bioinform. 2011, 4, 303-311.
149. Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C. et al., Predicting new molecular targets for known drugs. Nature 2009, 462, 175-181.
150. Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L. J., Bork, P., Drug target identification using side-effect similarity. Science 2008, 321, 263-266.
151. Van Driel, M. A., Bruggeman, J., Vriend, G., Brunner, H. G., Leunissen, J. A., A text-mining analysis of the human phenome. Eur. J. Hum. Genet. 2006, 14, 535-542.
152. Zhou, X., Menche, J., Barabási, A.-L., Sharma, A. Human symptoms-disease network. Nat. Commun. 2014, 5, 1-10.
153. Ding, H., Takigawa, I., Mamitsuka, H., Zhu, S., Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief. Bioinform. 2013, 15, 734-747.
154. Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., Kanehisa, M., Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 2008, 24, i232-i240.
155. Minai, R., Matsuo, Y., Onuki, H., Hirota, H., Method for comparing the structures of protein ligand-binding sites and application for predicting protein-drug interactions. Proteins 2008, 72, 367-381.
156. Chen, Y., Zhi, D., Ligand-protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins 2001, 43, 217-226.
157. Li, J., Zheng, S., Chen, B., Butte, A. J. et al., A survey of current trends in computational drug repositioning. Brief. Bioinform. 2015, 1, 11.
158. Li, Y., Jones, S., Drug repositioning for personalized medicine. Genome Med. 2012, 4, 27.
159. Berry, M. W., Browne, M., Langville, A. N., Pauca, V. P., Plemmons, R. J., Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat. Data Anal. 2007, 52, 155-173.
160. Fawcett, T., An introduction to ROC analysis. Pattern Recogn. Lett. 2006, 27, 861-874.
161. Vanunu, O., Magger, O., Ruppin, E., Shlomi, T., Sharan, R., Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 2010, 6, e1000641.
162. Wu, Z., Wang, Y., Chen, L., Network-based drug repositioning. Mol. BioSyst. 2013, 9, 1268-1281.
163. Del Vescovo, V., Grasso, M., Barbareschi, M., Denti, M. A., Micrornas as lung cancer biomarkers. World J. Clin. Oncol. 2014, 5, 604-620.
164. Hayes, J., Peruzzi, P. P., Lawler, S., Micrornas in cancer: biomarkers, functions and therapy. Trends Mol. Med. 2014, 20, 460-469.
165. Wen, X., Deng, F.-M., Wang, J., Micrornas as predictive biomarkers and therapeutic targets in prostate cancer. Am. J. Clin. Exp. Urol. 2014, 2, 219-230.
166. Mercer, T. R., Dinger, M. E., Mattick, J. S., Long non-coding RNAs: insights into functions. Nat. Rev. Genet. 2009, 10, 155-159.
167. Chen, X., Predicting lncRNA-disease associations and constructing lncrna functional similarity network based on the information of miRNA. Sci. Rep. 2009, 5, 13186.
168. Carlsson, G., Topology and data. B. Am. Math. Soc. 2009, 46, 255-308.
169. Lum, P. Y., Singh, G., Lehman, A., Ishkanov, T. et al., Extracting insights from the shape of complex data using topology. Sci. Rep. 2013, 3.
170. Hoens, T. R., Polikar, R., Chawla, N. V., Learning from streaming data with concept drift and imbalance: an overview. Prog. Artificial Intelligence 2012, 1, 89-101.
171. Stella, F., Amer, Y., Continuous time bayesian network classifiers. J. Biomed. Inform. 2012, 45, 1108-1119.
172. Ge, H., Walhout, A. J., Vidal, M., Integrating "omic" information: a bridge between genomics and systems biology. Trends Genet. 2003, 19, 551-560.
173. Mani, R., St.Onge, R. P., Hartman, J. L., Giaever, G., Roth, F. P., Defining genetic interaction. Proc. Natl. Acad. Sci. USA 2008, 105, 3461-3466.
174. Gligorijević, V., Janjić, V., Pržulj, N., Integration of molecular network data reconstruct gene ontology. Bioinformatics 2014, 30, i594-i600.
175. Ho, J. C., Ghosh, J., Sun, J., Marble: high-throughput phenotyping from electronic health records via sparse nonnegative tensor factorization. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ACM, New York, USA 2014, pp. 115-124.
176. Gottesman, O., Kuivaniemi, H., Tromp, G., Faucett, W. A. et al., The electronic medical records and genomics (emerge) network: past, present, and future. Genet. Med. 2013, 15, 761-771.
177. Vaudel, M., Verheggen, K., Csordas, A., Raeder, H. et al., Exploring the potential of public proteomics data. Proteomics 2016, 16, 214-225.