Methods for biological data integration: Perspectives and challenges

Journal of the Royal Society Interface

Volumn 12, Issue 112, 2015, Pages

  Gligorijević, Vladimir ^a   Pržulj, Nataša ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Biological networks; Data fusion; Heterogeneous data integration; Non negative matrix factorization; Omics data; Systems biology

Indexed keywords

COMPLEX NETWORKS; DATA FUSION; FACTORIZATION; MATRIX ALGEBRA;

BIOLOGICAL NETWORKS; HETEROGENEOUS DATA INTEGRATIONS; NONNEGATIVE MATRIX FACTORIZATION; OMICS DATA; SYSTEMS BIOLOGY;

DATA INTEGRATION;

CONTROLLED STUDY; MINING; PRACTICE GUIDELINE; FACTUAL DATABASE; INFORMATION PROCESSING; THEORETICAL MODEL;

AUTOMATIC DATA PROCESSING; DATABASES, FACTUAL; MODELS, THEORETICAL;

EID: 84948703087     PISSN: 17425689     EISSN: 17425662     Source Type: Journal    
DOI: 10.1098/rsif.2015.0571     Document Type: Review

References (195)

1. Ito T et al. 2000 Toward a protein-protein interaction map of the budding yeast: a comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. Proc. Natl Acad. Sci. USA 97, 1143-1147. (doi:10.1073/pnas.97.3.1143)
2. Uetz P et al. 2000 A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403, 623-627. (doi:10.1038/35001009)
3. Giot L et al. 2003 A protein interaction map of Drosophila melanogaster. Science 302, 1727-1736. (doi:10.1126/science.1090289)
4. Li S et al. 2004 A map of the interactome network of the metazoan C. elegans. Science 303, 540-543. (doi:10.1126/science.1091403)
5. Stelzl U et al. 2005 A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 957-968. (doi:10.1016/j.cell. 2005.08.029)
6. Simonis N et al. 2009 Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network. Nat. Methods 6, 47-54. (doi:10.1038/nmeth.1279)
7. Consortium AIM. 2011 Evidence for network evolution in an Arabidopsis interactome map. Science 333, 601-607. (doi:10.1126/science.1203877)
8. Gavin A et al. 2006 Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631-636. (doi:10.1038/nature04532)
9. Krogan N et al. 2006 Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637-643. (doi:10.1038/nature04670)
10. Hawkins RD, Hon GC, Ren B. 2010 Next-generation genomics: an integrative approach. Nat. Rev. Genet. 11, 476-486. (doi:10.1038/nrg2795)
11. Nielsen R, Paul JS, Albrechtsen A, Song YS. 2011 Genotype and SNP calling from next-generation sequencing data. Nat. Rev. Genet. 12, 443-451. (doi:10.1038/nrg2986)
12. Hirschhorn JN, Daly MJ. 2005 Genome-wide association studies for common diseases and complex traits. Nat. Rev. Genet. 6, 95-108. (doi:10. 1038/nrg1521)
13. Duerr RH et al. 2006 A genome-wide association study identifies IL23R as an inflammatory bowel disease gene. Science 314, 1461-1463. (doi:10. 1126/science.1135245)
14. Quackenbush J. 2001 Computational analysis of microarray data. Nat. Rev. Genet. 2, 418-427. (doi:10.1038/35076576)
15. Dahlquist KD, Salomonis N, Vranizan K, Lawlor SC, Conklin BR. 2002 GenMAPP, a new tool for viewing and analyzing microarray data on biological pathways. Nat. Genet. 31, 19-20. (doi:10.1038/ng0502-19)
16. Marioni JC, Mason CE, Mane SM, Stephens M, Gilad Y. 2008 RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509-1517. (doi:10.1101/gr.079558.108)
17. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. 2008 Mapping and quantifying mammalian transcriptomes by RNA-seq. Nat. Methods 5, 621-628. (doi:10.1038/nmeth.1226)
18. Wang Z, Gerstein M, Snyder M. 2009 RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57-63. (doi:10.1038/nrg2484)
19. Joyce AR, Palsson BO. 2006 The model organism as a system: integrating omics data sets. Nat. Rev. Mol. Cell Biol. 7, 198-210. (doi:10.1038/nrm1857)
20. Gomez-Cabrero D et al. 2014 Data integration in the era of omics: current and future challenges. BMC Syst. Biol. 8, I1. (doi:10.1186/1752-0509-8-S2-I1)
21. Vidal M, Cusick ME, Barabási A-L. 2011 Interactome networks and human disease. Cell 144, 986-998. (doi:10.1016/j.cell.2011.02.016)
22. Aittokallio T, Schwikowski B. 2006 Graph-based methods for analysing networks in cell biology. Brief Bioinformatics 7, 243-255. (doi:10.1093/bib/bbl022)
23. Przulj N. 2011 Protein-protein interactions: making sense of networks via graph-theoretic modeling. BioEssays 33, 115-123. (doi:10.1002/bies. 201000044)
24. Hakes L, Pinney JW, Robertson DL, Lovell SC. 2008 Protein-protein interaction networks and biology-what's the connection? Nat. Biotechnol. 26, 69-72. (doi:10.1038/nbt0108-69)
25. Tong AHY et al. 2004 Global mapping of the yeast genetic interaction network. Science 303, 808-813. (doi:10.1126/science.1091317)
26. Dixon SJ, Costanzo M, Baryshnikova A, Andrews B, Boone C. 2009 Systematic mapping of genetic interaction networks. Annu. Rev. Genet. 43, 601-625. (doi:10.1146/annurev.genet.39.073003.114751)
27. Costanzo M et al. 2010 The genetic landscape of a cell. Science 327, 425-431. (doi:10.1126/science. 1180823)
28. Tanaka R. 2005 Scale-rich metabolic networks. Phys. Rev. Lett. 94, 168101. (doi:10.1103/PhysRevLett.94.168101)
29. Ravasz E, Somera AL, Mongru DA, Oltvai ZN, Barabási A-L. 2002 Hierarchical organization of modularity in metabolic networks. Science 297, 1551-1555. (doi:10.1126/science.1073374)
30. Ma H, Zeng A-P. 2003 Reconstruction of metabolic networks from genome data and analysis of their global structure for various organisms. Bioinformatics 19, 270-277. (doi:10.1093/bioinformatics/19.2.270)
31. Wagner A, Fell DA. 2001 The small world inside large metabolic networks. Proc. R. Soc. Lond. B 268, 1803-1810. (doi:10.1098/rspb.2001.1711)
32. Prieto C, Risueo A, Fontanillo C, De Las Rivas J. 2008 Human gene coexpression landscape: confident network derived from tissue transcriptomic profiles. PLoS ONE 3, e3911. (doi:10. 1371/journal.pone.0003911)
33. Stuart JM, Segal E, Koller D, Kim SK. 2003 A genecoexpression network for global discovery of conserved genetic modules. Science 302, 249-255. (doi:10.1126/science.1087447)
34. Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q. 2008 GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9, S4. (doi:10.1186/gb-2008-9-s1-s4)
35. Linghu B, Snitkin E, Hu Z, Xia Y, DeLisi C. 2009 Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network. Genome Biol. 10, R91. (doi:10.1186/gb-2009-10-9-r91)
36. Pruitt KD, Tatusova T, Brown GR, Maglott DR. 2012 NCBI reference sequences (RefSeq): current status, new features and genome annotation policy. Nucleic Acids Res. 40, D130-D135. (doi:10.1093/nar/gkr1079)
37. Sladek R et al. 2007 A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881-885. (doi:10.1038/nature05616)
38. The Wellcome Trust Case Control Consortium. 2007 Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447, 661-678. (doi:10.1038/nature05911)
39. Weinstein JN et al. 2013 The cancer genome atlas pan-cancer analysis project. Nat. Genet. 45, 1113-1120. (doi:10.1038/ng.2764)
40. Zarrei M, Merico D, Scherer SW. 2015 A copy number variation map of the human genome. Nat. Rev. Genet. 16, 172-183. (doi:10.1038/nrg3871)
41. Ashburner M et al. 2000 Gene ontology: tool for the unification of biology. Nat. Genet. 25, 25-29. (doi:10.1038/75556)
42. Schriml LM, Arze C, Nadendla S, Chang Y-WW, Mazaitis M, Felix V, Feng G, Kibbe WA. 2012 Disease ontology: a backbone for disease semantic integration. Nucleic Acids Res. 40, D940-D946. (doi:10.1093/nar/gkr972)
43. Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat T, Weissig H, Shindyalov IN, Bourne PE. 2000 The protein data bank. Nucleic Acids Res. 28, 235-242. (doi:10.1093/nar/28.1.235)
44. Wishart DS, Knox C, Guo AC, Cheng D, Shrivastava S, Tzur D, Gautam B, Hassanali M. 2008 Drugbank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36(Suppl. 1), D901-D906. (doi:10.1093/nar/gkm958)
45. Davis AP et al. 2013 The comparative toxicogenomics database: update 2013. Nucleic Acids Res. 41, D1104-D1114. (doi:10.1093/nar/gks994)
46. Bolton EE, Wang Y, Thiessen PA, Bryant SH. 2008 PubChem: integrated platform of small molecules and biological activities. Annu. Rep. Comput. Chem. 4, 217-241. (doi:10.1016/S1574-1400(08)00012-1)
47. Albert R. 2007 Network inference, analysis, and modeling in systems biology. Plant Cell 19, 3327-3338. (doi:10.1105/tpc.107.054700)
48. Lee TI et al. 2002 Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 298, 799-804. (doi:10.1126/science.1075090)
49. Hecker M, Lambeck S, Toepfer S, Van Someren E, Guthke R. 2009 Gene regulatory network inference: data integration in dynamic models- A review. Biosystems 96, 86-103. (doi:10.1016/j.biosystems. 2008.12.004)
50. Lee I, Date SV, Adai AT, Marcotte EM. 2004 A probabilistic functional network of yeast genes. Science 306, 1555-1558. (doi:10.1126/science. 1099511)
51. Lanckriet GRG, De Bie T, Cristianini N, Jordan MI, Noble WS. 2004 A statistical framework for genomic data fusion. Bioinformatics 20, 2626-2635. (doi:10. 1093/bioinformatics/bth294)
52. Lanckriet G, Deng M, Cristianini N, Jordan M, Noble W. 2004 Kernel-based data fusion and its application to protein function prediction in yeast. In Biocomputing 2004, Proc. The Pacific Symp., Hawaii, USA, 6-10 January, pp. 300-311.
53. Ma X, Chen T, Sun F. 2013 Integrative approaches for predicting protein function and prioritizing genes for complex phenotypes using protein interaction networks. Brief. Bioinformatics 15, 685-698. (doi:10.1093/bib/bbt041)
54. Troyanskaya OG, Dolinski K, Owen AB, Altman RB, Botstein D. 2003 A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae). Proc. Natl Acad. Sci. USA 100, 8348-8353. (doi:10. 1073/pnas.0832373100)
55. Gligorijević V, Janjić V, Przulj N. 2014 Integration of molecular network data reconstruct gene ontology. Bioinformatics 30, i594-i600. (doi:10.1093/bioinformatics/btu470)
56. Nariai N, Kolaczyk ED, Kasif S. 2007 Probabilistic protein function prediction from heterogeneous genome-wide data. PLoS ONE 2, e337. (doi:10. 1371/journal.pone.0000337)
57. Wang H, Huang H, Ding C, Nie F. 2013 Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix trifactorization. J. Comput. Biol. 20, 344-358. (doi:10. 1089/cmb.2012.0273)
58. Köhler S, Bauer S, Horn D, Robinson PN. 2008 Walking the interactome for prioritization of candidate disease genes. Am. J. Hum. Genet. 82, 949-958. (doi:10.1016/j.ajhg.2008.02.013)
59. Bromberg Y. 2013 Chapter 15: disease gene prioritization. PLoS Comput. Biol. 9, e1002902. (doi:10.1371/journal.pcbi.1002902)
60. Hwang T, Atluri G, Xie M, Dey S, Hong C, Kumar V, Kuang R. 2012 Co-clustering phenomegenome for phenotype classification and disease gene discovery. Nucleic Acids Res. 40, e146. (doi:10.1093/nar/gks615)
61. Zitnik M, Janjić V, Chris L et al. 2013 Discovering disease-disease associations by fusing systemslevel molecular data. Sci. Rep. 3, 3202. (doi:10. 1038/srep03202)
62. Ashburn TT, Thor KB. 2004 Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3, 673-683. (doi:10. 1038/nrd1468)
63. Napolitano F, Zhao Y, Moreira VM, Tagliaferri R, Kere J, D'Amato M, Greco D. 2013 Drug repositioning: a machine learning approach through data integration. J. Cheminform. 5, 30. (doi:10.1186/1758-2946-5-30)
64. Hamburg MA, Collins FS. 2010 The path to personalized medicine. N. Engl. J. Med. 363, 301-304. (doi:10.1056/NEJMp1006304)
65. Ritchie MD, de Andrade M, Kuivaniemi H. 2015 The foundation of precision medicine: integration of electronic health records with genomics through basic, clinical, and translational research. Front. Genet. 6, 1-4. (doi:10.3389/fgene.2015.00104)
66. Dutkowski J, Kramer M, Surma MA, Balakrishnan R, Cherry JM, Krogan NJ, Ideker T. 2013 A gene ontology inferred from molecular networks. Nat. Biotechnol. 31, 38-45. (doi:10.1038/nbt.2463)
67. Sun K, Buchan N, Larminie C, Przulj N. 2014 The integrated disease network. Integr. Biol. 6, 1069-1079. (doi:10.1039/c4ib00122b)
68. Davis DA, Chawla NV. 2011 Exploring and exploiting disease interactions from multirelational gene and phenotype networks. PLoS ONE 6, e22670. (doi:10. 1371/journal.pone.0022670)
69. Guo X, Gao L, Wei C, Yang X, Zhao Y, Dong A. 2011 A computational method based on the integration of heterogeneous networks for predicting disease-gene associations. PLoS ONE 6, e24171. (doi:10. 1371/journal.pone.0024171)
70. Cheng F et al. 2012 Prediction of drug-target interactions and drug repositioning via networkbased inference. PLoS Comput. Biol. 8, e1002503. (doi:10.1371/journal.pcbi.1002503)
71. Emig D, Ivliev A, Pustovalova O, Lancashire L, Bureeva S, Nikolsky Y, Bessarabova M. 2013 Drug target prediction and repositioning using an integrated network-based approach. PLoS ONE 8, e60618. (doi:10.1371/journal.pone.0060618)
72. Vanunu O, Magger O, Ruppin E, Shlomi T, Sharan R. 2010 Associating genes and protein complexes with disease via network propagation. PLoS Comput. Biol. 6, e1000641. (doi:10.1371/journal.pcbi.1000641)
73. Huang Y-F, Yeh H-Y, Soo V-W. 2013 Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation. BMC Med. Genomics 6, 1-14. (doi:10.1186/1755-8794-6-S3-S4)
74. Rider AK, Chawla NV, Emrich SJ. 2013 A survey of current integrative network algorithms for systems biology, pp. 479-495. Amsterdam, The Netherlands: Springer.
75. Kim TY, Kim HU, Lee SY. 2010 Data integration and analysis of biological networks. Curr. Opin. Biotechnol. 21, 78-84. (doi:10.1016/j.copbio.2010. 01.003)
76. Bebek G, Koyutuerk M, Price ND, Chance MR. 2012 Network biology methods integrating biological data for translational science. Brief. Bioinformatics 13, 446-459. (doi:10.1093/bib/bbr075)
77. Hamid JS, Hu P, Roslin NM, Ling V, Greenwood CMT, Beyene J. 2009 Data integration in genetics and genomics: methods and challenges. Hum. Genomics Proteomics 2009, 869093. (doi:10.4061/2009/869093)
78. Kristensen VN, Lingjarde OC, Russnes HG, Vollan HKM, Frigessi A, Borresen-Dale A-L. 2014 Principles and methods of integrative genomic analyses in cancer. Nat. Rev. Cancer 14, 299-313. (doi:10. 1038/nrc3721)
79. Borgwardt K. 2011 Kernel methods in bioinformatics. In Handbook of statistical bioinformatics (eds HH-S Lu, B Schlkopf, H Zhao), Springer Handbooks of Computational Statistics, pp. 317-334. Berlin, Germany: Springer.
80. Needham CJ, Bradford JR, Bulpitt AJ, Westhead DR. 2007 A primer on learning in Bayesian networks for computational biology. PLoS Comput. Biol. 3, e129. (doi:10.1371/journal.pcbi.0030129)
81. Devarajan K. 2008 Nonnegative matrix factorization: an analytical and interpretive tool in computational biology. PLoS Comput. Biol. 4, e1000029. (doi:10. 1371/journal.pcbi.1000029)
82. Gligorijević V, Przulj N. 2015 Computational methods for integration of biological data. In Personalised medicine: a new medical and social challenge (eds N Bodiroga-Vukobrat, K Pavelic, D Rukavina, GG Sander). Berlin, Germany: Springer.
83. Alcaraz N, Pauling J, Batra R, Barbosa E, Junge A, Christensen A, Azevedo V, Ditzel HJ, Baumbach J. 2014 Keypathwayminer 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with cytoscape. BMC Syst. Biol. 8, 99. (doi:10.1186/s12918-014-0099-x)
84. Mitra K, Carvunis A-R, Ramesh SK, Ideker T. 2013 Integrative approaches for finding modular structure in biological networks. Nat. Rev. Genet. 14, 719-732. (doi:10.1038/nrg3552)
85. Jha A et al. 2015 Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419-430. (doi:10.1016/j.immuni. 2015.02.005)
86. Hwang D et al. 2005 A data integration methodology for systems biology. Proc. Natl Acad. Sci. USA 102, 17 296-17 301. (doi:10.1073/pnas. 0508647102)
87. West DB. 2000 Introduction to graph theory, 2nd edn. New York, NY: Prentice Hall.
88. Newman M. 2010 Networks: an introduction. New York, NY: Oxford University Press, Inc.
89. Das K. 2004 The Laplacian spectrum of a graph. Comput. Math. Appl. 48, 715-724. (doi:10.1016/j. camwa.2004.05.005)
90. Belkin M, Niyogi P, Sindhwani V. 2006 Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J. Mach. Learn. Res. 7, 2399-2434.
91. Albert R. 2005 Scale-free networks in cell biology. J. Cell Sci. 118, 4947-4957. (doi:10.1242/jcs.02714)
92. Barabasi A-L, Oltvai ZN. 2004 Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5, 101-113. (doi:10.1038/nrg1272)
93. Przulj N, Corneil DG, Jurisica I. 2004 Modeling interactome: scale-free or geometric? Bioinformatics 20, 3508-3515. (doi:10.1093/bioinformatics/bth436)
94. Higham DJ, Rasajski M, Przulj N. 2008 Fitting a geometric graph to a protein-protein interaction network. Bioinformatics 24, 1093-1099. (doi:10. 1093/bioinformatics/btn079)
95. Winterbach W, Mieghem PV, Reinders M, Wang H, Ridder DD. 2013 Topology of molecular interaction networks. BMC Syst. Biol. 7, 90. (doi:10.1186/1752-0509-7-90)
96. Chatr-Aryamontri A et al. 2013 The BioGRID interaction database. Nucleic Acids Res. 41, D816-D823. (doi:10.1093/nar/gks1158)
97. Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. 2012 KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res. 40, D109-D114. (doi:10.1093/nar/gkr988)
98. Knox C et al. 2011 DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res. 39(Suppl. 1), D1035-D1041. (doi:10.1093/nar/gkq1126)
99. Li MJ et al. 2012 GWASdb: a database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 40, D1047-D1054. (doi:10.1093/nar/gkr1182)
100. Denny JC et al. 2013 Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31, 1102-1111. (doi:10.1038/nbt.2749)
101. Barrett T et al. 2007 NCBI GEO: mining tens of millions of expression profiles database and tools update. Nucleic Acids Res. 35(Suppl. 1), D760-D765. (doi:10.1093/nar/gkl887)
102. Parkinson H et al. 2005 ArrayExpress-a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 33(Suppl. 1), D553-D555. (doi:10.1093/nar/gki056)
103. Hubble J et al. 2009 Implementation of GenePattern within the Stanford microarray database. Nucleic Acids Res. 37, D898-D901. (doi:10.1093/nar/gkn786)
104. Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. 2010 A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6, 343. (doi:10. 1038/msb.2009.98)
105. Phizicky EM, Fields S. 1995 Protein-protein interactions: methods for detection and analysis. Microbiol. Rev. 59, 94-123.
106. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási AL. 2000 The large-scale organization of metabolic networks. Nature 407, 651-654. (doi:10.1038/35036627)
107. Zhou T. 2013 Computational reconstruction of metabolic networks from Kegg. In Computational toxicology, vol. 930 (eds B Reisfeld, AN Mayeno), pp. 235-249. New York, NY: Humana Press.
108. Yildirim MA, Goh KI, Cusick ME, Barabási AL, Vidal M. 2007 Drug-target network. Nat. Biotechnol. 25, 1119-1126. (doi:10.1038/nbt1338)
109. Ziv ZB et al. 2003 Computational discovery of gene modules and regulatory networks. Nat. Biotechnol. 21, 1337-1342. (doi:10.1038/nbt890)
110. De Smet F, Mathys J, Marchal K, Thijs G, De Moor B, Moreau Y. 2002 Adaptive quality-based clustering of gene expression profiles. Bioinformatics 18, 735-746. (doi:10.1093/bioinformatics/18.5.735)
111. Luo F, Yang Y, Zhong J, Gao H, Khan L, Thompson D, Zhou J. 2007 Constructing gene co-expression networks and predicting functions of unknown genes by random matrix theory. BMC Bioinformatics 8, 299. (doi:10.1186/1471-2105-8-299)
112. Reverter A, Chan EK. 2008 Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks. Bioinformatics 24, 2491-2497. (doi:10. 1093/bioinformatics/btn482)
113. Baba K, Shibata R, Sibuya M. 2004 Partial correlation and conditional correlation as measures of conditional independence. Aust. N.Z. J. Stat. 46, 657-664. (doi:10.1111/j.1467-842X.2004.00360.x)
114. Cover TM, Thomas JA. 2012 Elements of information theory. New York, NY: John Wiley & Sons.
115. Willett P, Barnard JM, Downs GM. 1998 Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38, 983-996. (doi:10.1021/ci9800211)
116. Nikolova N, Jaworska J. 2003 Approaches to measure chemical similarity- A review. QSAR Combin. Sci. 22, 1006-1026. (doi:10.1002/qsar. 200330831)
117. Zhang P, Agarwal P, Obradovic Z. 2013 Computational drug repositioning by ranking and integrating multiple data sources, vol. 8190 of Lecture Notes in Computer Science, pp. 579-594. Berlin, Germany: Springer.
118. Atkinson HJ, Morris JH, Ferrin TE, Babbitt PC. 2009 Using sequence similarity networks for visualization of relationships across diverse protein superfamilies. PLoS ONE 4, e4345. (doi:10.1371/journal.pone. 0004345)
119. Valavanis I, Spyrou G, Nikita K. 2010 A similarity network approach for the analysis and comparison of protein sequence/structure sets. J. Biomed. Inform. 43, 257-267. (doi:10.1016/j.jbi. 2010.01.005)
120. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. 1990 Basic local alignment search tool. J. Mol. Biol. 215, 403-410. (doi:10.1006/jmbi.1990.9999)
121. Holm L, Park J. 2000 DaliLite workbench for protein structure comparison. Bioinformatics 16, 566-567. (doi:10.1093/bioinformatics/16.6.566)
122. Ge H, Walhout AJ, Vidal M. 2003 Integrating omic information: a bridge between genomics and systems biology. Trends Genet. 19, 551-560. (doi:10.1016/j.tig.2003.08.009)
123. Mani R, St.Onge RP, Hartman JL, Giaever G, Roth FP. 2008 Defining genetic interaction. Proc. Natl Acad. Sci. USA 105, 3461-3466. (doi:10.1073/pnas. 0712255105)
124. Mostafavi S, Morris Q. 2012 Combining many interaction networks to predict gene function and analyze gene lists. Proteomics 12, 1687-1696. (doi:10.1002/pmic.201100607)
125. Zhu J et al. 2008 Integrating large-scale functional genomic data to dissect the complexity of yeast regulatory networks. Nat. Genet. 40, 854-861. (doi:10.1038/ng.167)
126. Zhang B et al. 2013 Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimers disease. Cell 153, 707-720. (doi:10. 1016/j.cell.2013.03.030)
127. Jansen R et al. 2003 A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449-453. (doi:10.1126/science.1087361)
128. Gevaert O, Smet FD, Timmerman D, Moreau Y, Moor BD. 2006 Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks. Bioinformatics 22, e184-e190. (doi:10.1093/bioinformatics/btl230)
129. van Vliet MH, Horlings HM, van de Vijver MJ, Reinders MJ, Wessels LF. 2012 Integration of clinical and gene expression data has a synergetic effect on predicting breast cancer outcome. PLoS ONE 7, e40358. (doi:10.1371/journal.pone.0040358)
130. Yuan Y, Savage RS, Markowetz F. 2011 Patientspecific data fusion defines prognostic cancer subtypes. PLoS Comput. Biol. 7, e1002227. (doi:10. 1371/journal.pcbi.1002227)
131. Wang Y, Chen S, Deng N, Wang Y. 2013 Drug repositioning by kernel-based integration of molecular structure, molecular activity, and phenotype data. PLoS ONE 8, e78518. (doi:10.1371/journal.pone.0078518)
132. Kato T, Tsuda K, Asai K. 2005 Selective integration of multiple biological data for supervised network inference. Bioinformatics 21, 2488-2495. (doi:10. 1093/bioinformatics/bti339)
133. Yamanishi Y, Vert J-P, Kanehisa M. 2004 Protein network inference from multiple genomic data: a supervised approach. Bioinformatics 20(Suppl. 1), i363-i370. (doi:10.1093/bioinformatics/bth910)
134. Daemen A, Gevaert O, De Moor B. 2007 Integration of clinical and microarray data with kernel methods. In Engineering in medicine and biology society, 2007. EMBS 2007. 29th Annual Int. Conf. of the IEEE, pp. 5411-5415. Piscataway, NJ: IEEE.
135. Zitnik M, Zupan B. 2015 Data fusion by matrix factorization. IEEE Trans. Pattern Anal. Mach. Intell. 37, 41-53. (doi:10.1109/TPAMI.2014.2343973)
136. Pavlidis P, Cai J, Weston J, Noble WS. 2002 Learning gene functional classifications from multiple data types. J. Comput. Biol. 9, 401-411. (doi:10.1089/10665270252935539)
137. Ozen A, Gonen M, Alpaydin E, Haliloglu T. 2009 Machine learning integration for predicting the effect of single amino acid substitutions on protein stability. BMC Struct. Biol. 9, 66. (doi:10.1186/1472-6807-9-66)
138. Chen Y, Hao J, Jiang W, He T, Zhang X, Jiang T, Jiang R. 2013 Identifying potential cancer driver genes by genomic data integration. Sci. Rep. 3, 66. (doi:10.1038/srep03538)
139. Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabsi A-L. 2007 The human disease network. Proc. Natl Acad. Sci. USA 104, 8685-8690. (doi:10. 1073/pnas.0701361104)
140. Ben-Gal I. 2008 Bayesian networks. New York, NY: John Wiley & Sons, Ltd.
141. Sachs K, Perez O, Pe'er D, Lauffenburger DA, Nolan GP. 2005 Causal proteinsignaling networks derived from multiparameter single-cell data. Science 308, 523-529. (doi:10.1126/science.1105809)
142. Friedman N. 2004 Inferring cellular networks using probabilistic graphical models. Science 303, 799-805. (doi:10.1126/science.1094068)
143. Chickering DM. 1996 Learning Bayesian networks is NP-complete. In Learning from data, pp. 121-130. Berlin, Germany: Springer.
144. Cooper GF. 1990 The computational complexity of probabilistic inference using Bayesian belief networks. Artif. Intell. 42, 393-405. (doi:10.1016/0004-3702(90)90060-D)
145. Schadt E, Friend S, Shaywitz D. 2009 A network view of disease and compound screening. Nat. Rev. Drug Discov. 8, 286-295. (doi:10.1038/nrd2826)
146. Rockman MV, Kruglyak L. 2006 Genetics of global gene expression. Nat. Rev. Genet. 7, 862-872. (doi:10.1038/nrg1964)
147. Franceschini A et al. 2013 STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41, D808-D815. (doi:10.1093/nar/gks1094)
148. Schölkopf B, Tsuda K, Vert J-P. 2004 Kernel methods in computational biology. Cambridge, MA: MIT Press.
149. Golub GH, Van Loan CF. 2012 Matrix computations, vol. 3. Baltimore, MD: JHU Press.
150. Leslie CS, Eskin E, Noble WS. 2002 The spectrum kernel: a string kernel for SVM protein classification. In Pacific Symp. on Biocomputing, 3-7 January, pp. 566-575.
151. Leslie CS, Eskin E, Cohen A, Weston J, Noble WS. 2004 Mismatch string kernels for discriminative protein classification. Bioinformatics 20, 467-476. (doi:10.1093/bioinformatics/btg431)
152. Ben-Hur A, Brutlag D. 2003 Remote homology detection: a motif based approach. Bioinformatics 19(Suppl. 1), 26-i33. (doi:10.1093/bioinformatics/btg1002)
153. Gomez SM, Noble WS, Rzhetsky A. 2003 Learning to predict protein-protein interactions from protein sequences. Bioinformatics 19, 1875-1881. (doi:10. 1093/bioinformatics/btg352)
154. Kondor RI, Lafferty J. 2002 Diffusion kernels on graphs and other discrete structures. In Proc. The ICML, 8-12 July, pp. 315-322.
155. Hearst MA, Dumais ST, Osman E, Platt J, Scholkopf B. 1998 Support vector machines. IEEE Intell. Syst. Appl. 13, 18-28. (doi:10.1109/5254.708428)
156. Jolliffe I. 2005 Principal component analysis. New York, NY: Wiley Online Library.
157. Hardoon D, Szedmak S, Shawe-Taylor J. 2004 Canonical correlation analysis: an overview with application to learning methods. Neural Comput 16, 2639-2664. (doi:10.1162/0899766042321814)
158. Vapnik VN, Vapnik V. 1998 Statistical learning theory, vol. 1. New York, NY: Wiley.
159. Boser BE, Guyon IM, Vapnik VN. 1992 A training algorithm for optimal margin classifiers. In Proc. The Fifth Annual Workshop on Computational Learning Theory, 27-29 July, pp. 144-152. New York, NY: ACM.
160. Ben-Hur A, Ong CS, Sonnenburg S, Schölkopf B, Rätsch G. 2008 Support vector machines and kernels for computational biology. PLoS Comput. Biol. 4, e1000173. (doi:10.1371/journal.pcbi.1000173)
161. Noble WS et al. 2004 Support vector machine applications in computational biology. In Kernel methods in computational biology (eds B Schoelkopf, K Tsuda, J-P Vert), pp. 71-92. Cambridge, MA: MIT Press.
162. Gönen M, Alpaydin E. 2011 Multiple kernel learning algorithms. J. Mach. Learn. Res. 12, 2211-2268.
163. Wang X, Xing EP, Schaid DJ. 2014 Kernel methods for large-scale genomic data analysis. Brief. Bioinformatics 16, 183-192. (doi:10.1093/bib/bbu024)
164. Yu S, Tranchevent L.-C, Moor BD, Moreau Y. 2011 Kernel-based data fusion for machine learning- methods and applications in bioinformatics and text mining, vol. 345 of Studies in Computational Intelligence. Berlin, Germany: Springer.
165. Suykens JA, Vandewalle J. 1999 Least squares support vector machine classifiers. Neural Process. Lett. 9, 293-300. (doi:10.1023/A:1018628609742)
166. Lee DD, Seung HS. 1999 Learning the parts of objects by non-negative matrix factorization. Nature 401, 788-791. (doi:10.1038/44565)
167. Gersho A, Gray RM. 1992 Vector quantization and signal compression. Berlin, Germany: Springer Science & Business Media.
168. Cichocki A, Zdunek R, Phan AH, Amari S-I. 2009 Nonnegative matrix and tensor factorizations: applications to exploratory multi-way data analysis and blind source separation. New York, NY: John Wiley & Sons.
169. Ding C, He X, Simon HD. 2005 On the equivalence of nonnegative matrix factorization and spectral clustering. In Proc. The 2005 SIAM Int. Conf. on Data Mining, 21-23 April, pp. 606-610.
170. Zass R, Shashua A. 2005 A unifying approach to hard and probabilistic clustering. In Tenth IEEE Int. Conf. on Computer vision, 2005. ICCV 2005, vol. 1, pp. 294-301. Piscataway, NJ: IEEE.
171. Li T, Ding CHQ. 2013 Nonnegative matrix factorizations for clustering: a survey. In Data clustering: algorithms and applications, pp. 149-176. New York, NY: Chapman & Hall/CRC.
172. Liu W, Zheng N. 2004 Non-negative matrix factorization based methods for object recognition. Pattern Recogn. Lett. 25, 893-897. (doi:10.1016/j. patrec.2004.02.002)
173. Xu W, Liu X, Gong Y. 2003 Document clustering based on non-negative matrix factorization. In Proc. The 26th Annual Int. ACM Sigir Conf. on Research and Development in Information Retrieval, 28 July-1 August, pp. 267-273. New York, NY: ACM.
174. Shahnaz F, Berry MW, Pauca VP, Plemmons RJ. 2006 Document clustering using nonnegative matrix factorization. Inf. Process. Manage. 42, 373-386. (doi:10.1016/j.ipm.2004.11.005)
175. Smaragdis P, Brown JC. 2003 Non-negative matrix factorization for polyphonic music transcription. In Applications of signal processing to audio and acoustics, pp. 177-180. Piscataway, NJ: IEEE.
176. Virtanen T. 2007 Monaural sound source separation by nonnegative matrix factorization with temporal continuity and sparseness criteria. IEEE Trans. Audio, Speech Lang. Process. 15, 1066-1074. (doi:10. 1109/TASL.2006.885253)
177. Brunet J-P, Tamayo P, Golub TR et al. 2004 Metagenes and molecular pattern discovery using matrix factorization. Proc. Natl Acad. Sci. USA 101, 4164-4169. (doi:10.1073/pnas. 0308531101)
178. Hutchins LN, Murphy SM, Singh P, Graber JH. 2008 Position-dependent motif characterization using non-negative matrix factorization. Bioinformatics 24, 2684-2690. (doi:10.1093/bioinformatics/btn526)
179. Koren Y, Bell R, Volinsky C. 2009 Matrix factorization techniques for recommender systems. Computer 42, 30-37. (doi:10.1109/MC.2009.263)
180. Zhang S, Wang W, Ford J, Makedon F. 2006 Learning from incomplete ratings using nonnegative matrix factorization. In SDM, pp. 549-553. Bethesda, MD: SIAM.
181. Cheng C, Yang H, King I, Lyu MR. 2012 Fused matrix factorization with geographical and social influence in location-based social networks. In Aaai'12, 22-26 July, pp. 1-1.
182. Li T, Zhang Y, Sindhwani V. 2009 A non-negative matrix tri-factorization approach to sentiment classification with lexical prior knowledge. In Proc. The Joint Conf. of the 47th Annual Meeting of the ACL and the 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, 2-7 August, pp. 244-252. Association for Computational Linguistics.
183. Ding C et al. 2006 Orthogonal nonnegative matrix Tri-factorizations for clustering. In Proc. The 12th ACM SIGKDD Int. Conf. On Knowledge Discovery and Data Mining, KDD '06, 20-23 August, pp. 126-135. New York, NY: ACM.
184. Johnson CR. 1990 Matrix completion problems: a survey. In Matrix theory and applications, vol. 40 of Proceedings of Symposia in Applied Mathematics, pp. 171-198. Providence, RI: AMS.
185. Vavasis SA. 2009 On the complexity of nonnegative matrix factorization. SIAM J. Optim. 20, 1364-1377. (doi:10.1137/070709967)
186. Berry MW, Browne M, Langville AN, Pauca VP, Plemmons RJ. 2007 Algorithms and applications for approximate nonnegative matrix factorization. Comput. Stat Data Anal. 52, 155-173. (doi:10. 1016/j.csda.2006.11.006)
187. Ding C, Li T, Jordan MI. 2010 Convex and seminonnegative matrix factorizations. IEEE Trans. Pattern Anal. Mach. Intell. 32, 45-55. (doi:10.1109/TPAMI.2008.277)
188. Sun W, Yuan Y.-X. 2006 Optimization theory and methods: nonlinear programming, vol. 1. Berlin, Germany: Springer Science & Business Media.
189. Lee DD, Seung HS. 2001 Algorithms for nonnegative matrix factorization. In Advances in neural information processing systems, pp. 556-562. Cambdrige, MA: MIT Press.
190. Albright R, Cox J, Duling D, Langville A, Meyer C. 2006 Algorithms, initializations, and convergence for the nonnegative matrix factorization. Tech. Rep, 81706. North Carolina State University, Raleigh, N.C.
191. Boyd S, Vandenberghe L. 2004 Convex optimization. New York, NY: Cambridge University Press.
192. Wang H, Huang H, Ding C. 2011 Simultaneous clustering of multi-type relational data via symmetric nonnegative matrix tri-factorization. In Proc. The 20th ACM Int. Conf. on Information and Knowledge Management, CIKM '11, pp. 279-284. New York, NY: ACM.
193. Wang F, Li T, Zhang C. 2008 Semi-supervised clustering via matrix factorization. In SDM, pp. 1-12. Atlanta, GA: SIAM.
194. Cai D, He X, Han J, Huang TS. 2011 Graph regularized nonnegative matrix factorization for data representation. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1548-1560. (doi:10.1109/TPAMI. 2010.231)
195. Shang F, Jiao L, Wang F. 2012 Graph dual regularization non-negative matrix factorization for co-clustering. Pattern Recognit. 45, 2237-2250. (doi:10.1016/j.patcog.2011.12.015)