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Volume 43 Issue 6
Jun.  2016
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Article Navigation >  Journal of Genetics and Genomics  > 2016  >  43(6): 349-367

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

doi: 10.1016/j.jgg.2015.11.002
  • a 

    Department of Biostatistics, Epidemiology and Scientific Computing, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia

  • b 

    Department of Genetics, King Faisal Specialist Hospital and Research Centre, Riyadh 11211, Saudi Arabia

  • c 

    College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia

More Information
  • Corresponding author: E-mail address: dcolakkaya@kfshrc.edu.sa (Dilek Colak)
  • Received Date: 2015-06-11
  • Accepted Date: 2015-11-20
  • Rev Recd Date: 2015-10-22
    • Available Online: 2015-12-15
  • Publish Date: 2016-06-20
    Abstract
  • A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.
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  • [1]
    Adie, E.A., Adams, R.R., Evans, K.L. et al. SUSPECTS: enabling fast and effective prioritization of positional candidates Bioinformatics, 22 (2006),pp. 773-774
    [2]
    Aerts, S., Lambrechts, D., Maity, S. et al. Gene prioritization through genomic data fusion Nat. Biotechnol., 24 (2006),pp. 537-544
    [3]
    Akavia, U.D., Litvin, O., Kim, J. et al. An integrated approach to uncover drivers of cancer Cell, 143 (2010),pp. 1005-1017
    [4]
    Alon, N., Yuster, R., Zwick, U. Color-coding J. ACM, 42 (1995),pp. 844-856
    [5]
    Amar, D., Shamir, R. Constructing module maps for integrated analysis of heterogeneous biological networks Nucleic Acids Res., 42 (2014),pp. 4208-4219
    [6]
    Amberger, J.S., Bocchini, C.A., Schiettecatte, F. et al. Nucleic Acids Res., 43 (2015),pp. D789-D798
    [7]
    Backes, C., Rurainski, A., Klau, G.W. et al. An integer linear programming approach for finding deregulated subgraphs in regulatory networks Nucleic Acids Res., 40 (2012),p. e43
    [8]
    Bader, J.S. Greedily building protein networks with confidence Bioinformatics, 19 (2003),pp. 1869-1874
    [9]
    Bair, E., Tibshirani, R. Semi-supervised methods to predict patient survival from gene expression data PLoS Biol., 2 (2004),p. e108
    [10]
    Barabási, A.-L., Gulbahce, N., Loscalzo, J. Network medicine: a network-based approach to human disease Nat. Rev. Genet., 12 (2011),pp. 56-68
    [11]
    Barrenas, F., Chavali, S., Holme, P. et al. Network properties of complex human disease genes identified through genome-wide association studies PLoS One, 4 (2009),p. e8090
    [12]
    Bastian, M., Heymann, S., Jacomy, M. Gephi: an open source software for exploring and manipulating networks ICWSM, 8 (2009),pp. 361-362
    [13]
    Batagelj, V., Mrvar, A.
    [14]
    Bauer-Mehren, A., Bundschus, M., Rautschka, M. et al. Gene-disease network analysis reveals functional modules in Mendelian, complex and environmental diseases PLoS One, 6 (2011),p. e20284
    [15]
    Bauer-Mehren, A., Rautschka, M., Sanz, F. et al. DisGeNET: a Cytoscape plugin to visualize, integrate, search and analyze gene–disease networks Bioinformatics, 26 (2010),pp. 2924-2926
    [16]
    Beagley, N., Stratton, K.G., Webb-Robertson, B.-J.M. VIBE 2.0: visual integration for Bayesian evaluation Bioinformatics, 26 (2010),pp. 280-282
    [17]
    Becker, K.G., Barnes, K.C., Bright, T.J. et al. The genetic association database Nat. Genet., 36 (2004),pp. 431-432
    [18]
    Beisser, D., Klau, G.W., Dandekar, T. et al. BioNet: an R-Package for the functional analysis of biological networks Bioinformatics, 26 (2010),pp. 1129-1130
    [19]
    Berger, S.I., Iyengar, R., Ma’ayan, A. AVIS: AJAX viewer of interactive signaling networks Bioinformatics, 23 (2007),pp. 2803-2805
    [20]
    Bonetta, L. Protein–protein interactions: interactome under construction Nature, 468 (2010),pp. 851-854
    [21]
    Breitkreutz, B.-J., Stark, C., Tyers, M. Osprey: a network visualization system Genome Biol., 4 (2003),p. R22
    [22]
    Brown, K.R., Jurisica, I. Online predicted human interaction database Bioinformatics, 21 (2005),pp. 2076-2082
    [23]
    Brown, K.R., Otasek, D., Ali, M. et al. NAViGaTOR: network analysis, visualization and graphing Toronto Bioinformatics, 25 (2009),pp. 3327-3329
    [24]
    Bundschus, M., Dejori, M., Stetter, M. et al. Extraction of semantic biomedical relations from text using conditional random fields BMC Bioinformatics, 9 (2008),p. 207
    [25]
    Butte, A.J. Medicine. The ultimate model organism Science, 320 (2008),pp. 325-327
    [26]
    Chatr-Aryamontri, A., Breitkreutz, B.J., Oughtred, R. et al. The BioGRID interaction database: 2015 update Nucleic Acids Res., 43 (2015),pp. D470-D478
    [27]
    Chen, H., Zhang, Z. Similarity-based methods for potential human microRNA-disease association prediction BMC Med. Genomics, 6 (2013),p. 12
    [28]
    Chen, J., Aronow, B.J., Jegga, A.G. Disease candidate gene identification and prioritization using protein interaction networks BMC Bioinformatics, 10 (2009),p. 73
    [29]
    Chen, J., Xu, H., Aronow, B.J. et al. Improved human disease candidate gene prioritization using mouse phenotype BMC Bioinformatics, 8 (2007),p. 392
    [30]
    Chen, L., Xuan, J., Riggins, R.B. et al. Identifying protein interaction subnetworks by a bagging Markov random field-based method Nucleic Acids Res., 41 (2013),p. e42
    [31]
    Chuang, H.Y., Lee, E., Liu, Y.T. et al. Network-based classification of breast cancer metastasis Mol. Syst. Biol., 3 (2007),p. 140
    [32]
    Chuang, H.Y., Rassenti, L., Salcedo, M. et al. Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression Blood, 120 (2012),pp. 2639-2649
    [33]
    Colak, D., Al-Dhalaan, H., Nester, M. et al. Genomic and transcriptomic analyses distinguish classic Rett and Rett-like syndrome and reveals shared altered pathways Genomics, 97 (2011),pp. 19-28
    [34]
    Colak, D., Chishti, M.A., Al-Bakheet, A.B. et al. Integrative and comparative genomics analysis of early hepatocellular carcinoma differentiated from liver regeneration in young and old Mol. Cancer, 9 (2010),p. 146
    [35]
    Colak, D., Kaya, N., Al-Zahrani, J. et al. Left ventricular global transcriptional profiling in human end-stage dilated cardiomyopathy Genomics, 94 (2009),pp. 20-31
    [36]
    Costa Pereira, J., Coviello, E., Doyle, G. et al. On the role of correlation and abstraction in cross-modal multimedia retrieval IEEE Trans. Pattern Anal. Mach. Intell., 36 (2014),pp. 521-535
    [37]
    Cotney, J., Muhle, R.A., Sanders, S.J. et al. The autism-associated chromatin modifier CHD8 regulates other autism risk genes during human neurodevelopment Nat. Commun., 6 (2015),p. 6404
    [38]
    Cozzini, A., Jasra, A., Montana, G. Model-based clustering with gene ranking using penalized mixtures of heavy-tailed distributions J. Bioinform. Comput. Biol., 11 (2013),p. 1341007
    [39]
    Croft, D., Mundo, A.F., Haw, R. et al. The reactome pathway knowledgebase Nucleic Acids Res., 42 (2014),pp. D472-D477
    [40]
    Dal Moro, F., Abate, A., Lanckriet, G.R. et al. A novel approach for accurate prediction of spontaneous passage of ureteral stones: support vector machines Kidney Int., 69 (2006),pp. 157-160
    [41]
    Dannenfelser, R., Clark, N.R., Ma'ayan, A. Genes2FANs: connecting genes through functional association networks BMC Bioinformatics, 13 (2012),p. 156
    [42]
    Dao, P., Wang, K., Collins, C. et al. Optimally discriminative subnetwork markers predict response to chemotherapy Bioinformatics, 27 (2011),pp. i205-i213
    [43]
    Daraselia, N., Yuryev, A., Egorov, S. et al. Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks BMC Bioinformatics, 8 (2007),p. 243
    [44]
    Dave, S.S. Gene expression signatures and outcome prediction in mature B-cell malignancies Curr. Treat Options Oncol., 7 (2006),pp. 261-269
    [45]
    Dave, S.S., Fu, K., Wright, G.W. et al. Molecular diagnosis of Burkitt's lymphoma N. Engl. J. Med., 354 (2006),pp. 2431-2442
    [46]
    Davis, A.P., Grondin, C.J., Lennon-Hopkins, K. et al. The comparative toxicogenomics database's 10th year anniversary: update 2015 Nucleic Acids Res., 43 (2015),pp. D914-D920
    [47]
    de Matos Simoes, R., Emmert-Streib, F. Bagging statistical network inference from large-scale gene expression data PLoS One, 7 (2012),p. e33624
    [48]
    Dezső, Z., Nikolsky, Y., Nikolskaya, T. et al. Identifying disease-specific genes based on their topological significance in protein networks BMC Syst. Biol., 3 (2009),p. 36
    [49]
    Diez, D., Wheelock, A.M., Goto, S. et al. The use of network analyses for elucidating mechanisms in cardiovascular disease Mol. Biosyst., 6 (2010),pp. 289-304
    [50]
    Ding, Y., Chen, M., Liu, Z. et al. atBioNet–an integrated network analysis tool for genomics and biomarker discovery BMC Genomics, 13 (2012),p. 325
    [51]
    Enright, A.J., Van Dongen, S., Ouzounis, C.A. An efficient algorithm for large-scale detection of protein families Nucleic Acids Res., 30 (2002),pp. 1575-1584
    [52]
    Eppig, J.T., Blake, J.A., Bult, C.J. et al. The Mouse Genome Database (MGD): comprehensive resource for genetics and genomics of the laboratory mouse Nucleic Acids Res., 40 (2012),pp. D881-D886
    [53]
    Ergun, A., Lawrence, C.A., Kohanski, M.A. et al. A network biology approach to prostate cancer Mol. Syst. Biol., 3 (2007),p. 82
    [54]
    Erten, S., Chowdhury, S.A., Guan, X. et al. Identifying stage-specific protein subnetworks for colorectal cancer BMC Proc., 6 (2012),p. S1
    [55]
    Ewing, R.M., Chu, P., Elisma, F. et al. Large-scale mapping of human protein–protein interactions by mass spectrometry Mol. Syst. Biol., 3 (2007),p. 89
    [56]
    Finak, G., Bertos, N., Pepin, F. et al. Stromal gene expression predicts clinical outcome in breast cancer Nat. Med., 14 (2008),pp. 518-527
    [57]
    Formstecher, E., Aresta, S., Collura, V. et al. Genome Res., 15 (2005),pp. 376-384
    [58]
    Franceschini, A., Szklarczyk, D., Frankild, S. et al. STRING v9. 1: protein-protein interaction networks, with increased coverage and integration Nucleic Acids Res., 41 (2013),pp. D808-D815
    [59]
    Franke, L., Van Bakel, H., Fokkens, L. et al. Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes Am. J. Hum. Genet., 78 (2006),p. 1011
    [60]
    Frey, B.J., Dueck, D. Clustering by passing messages between data points Science, 315 (2007),pp. 972-976
    [61]
    Gao, S., Wang, X. Identification of highly synchronized subnetworks from gene expression data BMC Bioinformatics, 14 (2013),p. S5
    [62]
    Garcia-Garcia, J., Guney, E., Aragues, R. et al. Biana: a software framework for compiling biological interactions and analyzing networks BMC Bioinformatics, 11 (2010),p. 56
    [63]
    Garraway, L.A., Widlund, H.R., Rubin, M.A. et al. Integrative genomic analyses identify MITF as a lineage survival oncogene amplified in malignant melanoma Nature, 436 (2005),pp. 117-122
    [64]
    Gillis, J., Pavlidis, P. “Guilt by association” is the exception rather than the rule in gene networks PLoS Comput. Biol., 8 (2012),p. e1002444
    [65]
    Glaab, E., Baudot, A., Krasnogor, N. et al. EnrichNet: network-based gene set enrichment analysis Bioinformatics, 28 (2012),pp. i451-i457
    [66]
    Glass, K., Huttenhower, C., Quackenbush, J. et al. Passing messages between biological networks to refine predicted interactions PLoS One, 8 (2013),p. e64832
    [67]
    Goh, K.I., Cusick, M.E., Valle, D. et al. The human disease network Proc. Natl. Acad. Sci. USA, 104 (2007),pp. 8685-8690
    [68]
    Green, P.J., Richardson, S. Modelling heterogeneity with and without the dirichlet process Scand. J. Stat., 28 (2001),pp. 355-375
    [69]
    Güldener, U., Münsterkötter, M., Oesterheld, M. et al. MPact: the MIPS protein interaction resource on yeast Nucleic Acids Res., 34 (2006),pp. D436-D441
    [70]
    Guney, E., Oliva, B. Exploiting protein-protein interaction networks for genome-wide disease-gene prioritization PLoS One, 7 (2012),p. e43557
    [71]
    Guo, Z., Li, Y., Gong, X. et al. Edge-based scoring and searching method for identifying condition-responsive protein–protein interaction sub-network Bioinformatics, 23 (2007),pp. 2121-2128
    [72]
    Haibe-Kains, B., Olsen, C., Djebbari, A. et al. Predictive networks: a flexible, open source, web application for integration and analysis of human gene networks Nucleic Acids Res., 40 (2012),pp. D866-D875
    [73]
    Han, K., Park, B., Kim, H. et al. HPID: the Human Protein Interaction Database Bioinformatics, 20 (2004),pp. 2466-2470
    [74]
    Hayasaka, S., Hugenschmidt, C.E., Laurienti, P.J. A network of genes, genetic disorders, and brain areas PLoS One, 6 (2011),p. e20907
    [75]
    He, Z., Zhang, J., Shi, X.-H. et al. Predicting drug-target interaction networks based on functional groups and biological features PLoS One, 5 (2010),p. e9603
    [76]
    Hedenfalk, I., Duggan, D., Chen, Y. et al. Gene-expression profiles in hereditary breast cancer N. Engl. J. Med., 344 (2001),pp. 539-548
    [77]
    Hidalgo, C.A., Blumm, N., Barabasi, A.L. et al. A dynamic network approach for the study of human phenotypes PLoS Comput. Biol., 5 (2009),p. e1000353
    [78]
    Ho, C.Y., Seidman, C.E. A contemporary approach to hypertrophic cardiomyopathy Circulation, 113 (2006),pp. e858-862
    [79]
    Hooper, S.D., Bork, P. Medusa: a simple tool for interaction graph analysis Bioinformatics, 21 (2005),pp. 4432-4433
    [80]
    Hu, G., Agarwal, P. Human disease-drug network based on genomic expression profiles PLoS One, 4 (2009),p. e6536
    [81]
    Hu, Z., Mellor, J., Wu, J. et al. VisANT: an online visualization and analysis tool for biological interaction data BMC Bioinformatics, 5 (2004),p. 17
    [82]
    Huttenhower, C., Schroeder, M., Chikina, M.D. et al. The Sleipnir library for computational functional genomics Bioinformatics, 24 (2008),pp. 1559-1561
    [83]
    Ideker, T., Ozier, O., Schwikowski, B. et al. Discovering regulatory and signalling circuits in molecular interaction networks Bioinformatics (2002),pp. S233-S240
    [84]
    Ideker, T., Sharan, R. Protein networks in disease Genome Res., 18 (2008),pp. 644-652
    [85]
    Isserlin, R., El-Badrawi, R.A., Bader, G.D. The biomolecular interaction network database in PSI-MI 2.5 Database (Oxford), 2011 (2011)
    [86]
    Jensen, F.V.
    [87]
    Jia, P., Zheng, S., Long, J. et al. dmGWAS: dense module searching for genome-wide association studies in protein–protein interaction networks Bioinformatics, 27 (2011),pp. 95-102
    [88]
    Jiang, B., Gribskov, M. Assessment of subnetwork detection methods for breast cancer Cancer Inform., 13 (2014),p. 15
    [89]
    Jiang, C., Xuan, Z., Zhao, F. et al. TRED: a transcriptional regulatory element database, new entries and other development Nucleic Acids Res., 35 (2007),pp. D137-D140
    [90]
    Joyce, A.R., Palsson, B.O. The model organism as a system: integrating ‘omics’ data sets Nat. Rev. Mol. Cell Biol., 7 (2006),pp. 198-210
    [91]
    Kacprowski, T., Doncheva, N.T., Albrecht, M. NetworkPrioritizer: a versatile tool for network-based prioritization of candidate disease genes or other molecules Bioinformatics, 29 (2013),pp. 1471-1473
    [92]
    Kanehisa, M., Goto, S., Sato, Y. et al. Data, information, knowledge and principle: back to metabolism in KEGG Nucleic Acids Res., 42 (2014),pp. D199-D205
    [93]
    Kang, H.J., Kawasawa, Y.I., Cheng, F. et al. Spatio-temporal transcriptome of the human brain Nature, 478 (2011),pp. 483-489
    [94]
    Kerrien, S., Orchard, S., Montecchi-Palazzi, L. et al. Broadening the horizon–level 2.5 of the HUPO-PSI format for molecular interactions BMC Biol., 5 (2007),p. 44
    [95]
    Kim, Y., Kim, T.-K., Kim, Y. et al. Principal network analysis: identification of subnetworks representing major dynamics using gene expression data Bioinformatics, 27 (2011),pp. 391-398
    [96]
    King, A.D., Pržulj, N., Jurisica, I. Bioinformatics, 20 (2004),pp. 3013-3020
    [97]
    Kirk, P., Griffin, J.E., Savage, R.S. et al. Bayesian correlated clustering to integrate multiple datasets Bioinformatics, 28 (2012),pp. 3290-3297
    [98]
    Köhler, J., Baumbach, J., Taubert, J. et al. Graph-based analysis and visualization of experimental results with ONDEX Bioinformatics, 22 (2006),pp. 1383-1390
    [99]
    Köhler, S., Bauer, S., Horn, D. et al. Walking the interactome for prioritization of candidate disease genes Am. J. Hum. Genet., 82 (2008),p. 949
    [100]
    Komurov, K., Dursun, S., Erdin, S. et al. NetWalker: a contextual network analysis tool for functional genomics BMC Genomics, 13 (2012),p. 282
    [101]
    Komurov, K., White, M. Revealing static and dynamic modular architecture of the eukaryotic protein interaction network Mol. Syst. Biol., 3 (2007),p. 110
    [102]
    Lage, K., Karlberg, E.O., Størling, Z.M. et al. A human phenome-interactome network of protein complexes implicated in genetic disorders Nat. Biotechnol., 25 (2007),pp. 309-316
    [103]
    Lanckriet, G.R., De Bie, T., Cristianini, N. et al. A statistical framework for genomic data fusion Bioinformatics, 20 (2004),pp. 2626-2635
    [104]
    Langfelder, P., Horvath, S. WGCNA: an R package for weighted correlation network analysis BMC Bioinformatics, 9 (2008),p. 559
    [105]
    Larkin, J.E., Frank, B.C., Gaspard, R.M. et al. Cardiac transcriptional response to acute and chronic angiotensin II treatments Physiol. Genomics, 18 (2004),pp. 152-166
    [106]
    Le, D.H., Kwon, Y.K. Neighbor-favoring weight reinforcement to improve random walk-based disease gene prioritization Comput. Biol. Chem., 44 (2013),pp. 1-8
    [107]
    Lee, D.S., Park, J., Kay, K.A. et al. The implications of human metabolic network topology for disease comorbidity Proc. Natl. Acad. Sci. USA, 105 (2008),pp. 9880-9885
    [108]
    Lee, I., Blom, U.M., Wang, P.I. et al. Prioritizing candidate disease genes by network-based boosting of genome-wide association data Genome Res., 21 (2011),pp. 1109-1121
    [109]
    Lee, I., Date, S.V., Adai, A.T. et al. A probabilistic functional network of yeast genes Science, 306 (2004),pp. 1555-1558
    [110]
    Lee, W.P., Tzou, W.S. Computational methods for discovering gene networks from expression data Brief. Bioinform., 10 (2009),pp. 408-423
    [111]
    Li, G.-L., Xu, X.-H., Wang, B.-A. et al. Analysis of protein-protein interaction network and functional modules on primary osteoporosis Eur. J. Med. Res., 19 (2014),p. 15
    [112]
    Li, Y., Agarwal, P. A pathway-based view of human diseases and disease relationships PLoS One, 4 (2009),p. e4346
    [113]
    Licata, L., Briganti, L., Peluso, D. et al. MINT, the molecular interaction database: 2012 update Nucleic Acids Res., 40 (2012),pp. D857-D861
    [114]
    Lichtenstein, I., Charleston, M.A., Caetano, T.S. et al. Active subnetwork recovery with a mechanism-dependent scoring function; with application to angiogenesis and organogenesis studies BMC Bioinformatics, 14 (2013),p. 59
    [115]
    Linghu, B., Snitkin, E.S., Hu, Z. et al. Genome-wide prioritization of disease genes and identification of disease-disease associations from an integrated human functional linkage network Genome Biol., 10 (2009),p. R91
    [116]
    Liu, G., Wong, L., Chua, H.N. Complex discovery from weighted PPI networks Bioinformatics, 25 (2009),pp. 1891-1897
    [117]
    Liu, K.Q., Liu, Z.P., Hao, J.K. et al. Identifying dysregulated pathways in cancers from pathway interaction networks BMC Bioinformatics, 13 (2012),p. 126
    [118]
    Liu, Y., Koyuturk, M., Barnholtz-Sloan, J.S. et al. Gene interaction enrichment and network analysis to identify dysregulated pathways and their interactions in complex diseases BMC Syst. Biol., 6 (2012),p. 65
    [119]
    Loscalzo, J., Kohane, I., Barabasi, A.L. Human disease classification in the postgenomic era: a complex systems approach to human pathobiology Mol. Syst. Biol., 3 (2007),p. 124
    [120]
    Lu, L.J., Xia, Y., Paccanaro, A. et al. Assessing the limits of genomic data integration for predicting protein networks Genome Res., 15 (2005),pp. 945-953
    [121]
    Lu, M., Zhang, Q., Deng, M. et al. An analysis of human microRNA and disease associations PLoS One, 3 (2008),p. e3420
    [122]
    Macropol, K., Can, T., Singh, A.K. RRW: repeated random walks on genome-scale protein networks for local cluster discovery BMC Bioinformatics, 10 (2009),p. 283
    [123]
    Madhamshettiwar, P.B., Maetschke, S.R., Davis, M.J. et al. RMaNI: regulatory module network inference framework BMC Bioinformatics, 14 (2013),p. S14
    [124]
    Mi, H., Muruganujan, A., Thomas, P.D. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees Nucleic Acids Res., 41 (2013),pp. D377-D386
    [125]
    Mitra, K., Carvunis, A.-R., Ramesh, S.K. et al. Integrative approaches for finding modular structure in biological networks Nat. Rev. Genet., 14 (2013),pp. 719-732
    [126]
    Narayanan, M., Vetta, A., Schadt, E.E. et al. Simultaneous clustering of multiple gene expression and physical interaction datasets PLoS Comput. Biol., 6 (2010),p. e1000742
    [127]
    Nayak, L., Tunga, H., De, R.K. Disease co-morbidity and the human Wnt signaling pathway: a network-wise study OMICS, 17 (2013),pp. 318-337
    [128]
    Nepusz, T., Yu, H., Paccanaro, A. Detecting overlapping protein complexes in protein-protein interaction networks Nat. Methods, 9 (2012),pp. 471-472
    [129]
    Newman, M.E., Girvan, M. Finding and evaluating community structure in networks Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 69 (2004),p. 026113
    [130]
    Nguyen, T.P., Caberlotto, L., Morine, M.J. et al. Network analysis of neurodegenerative disease highlights a role of toll-like receptor signaling Biomed. Res. Int., 2014 (2014),p. 686505
    [131]
    Nibbe, R.K., Koyuturk, M., Chance, M.R. An integrative -omics approach to identify functional sub-networks in human colorectal cancer PLoS Comput. Biol., 6 (2010),p. e1000639
    [132]
    Oliver, S. Proteomics: guilt-by-association goes global Nature, 403 (2000),pp. 601-603
    [133]
    Orchard, S., Ammari, M., Aranda, B. et al. The MIntAct project—IntAct as a common curation platform for 11 molecular interaction databases Nucleic Acids Res., 42 (2014),pp. D358-D363
    [134]
    Oti, M., Snel, B., Huynen, M.A. et al. Predicting disease genes using protein–protein interactions J. Med. Genet., 43 (2006),pp. 691-698
    [135]
    Pagel, P., Kovac, S., Oesterheld, M. et al. The MIPS mammalian protein–protein interaction database Bioinformatics, 21 (2005),pp. 832-834
    [136]
    Palla, G., Derényi, I., Farkas, I. et al. Uncovering the overlapping community structure of complex networks in nature and society Nature, 435 (2005),pp. 814-818
    [137]
    Patil, M.A., Chua, M.S., Pan, K.H. et al. An integrated data analysis approach to characterize genes highly expressed in hepatocellular carcinoma Oncogene, 24 (2005),pp. 3737-3747
    [138]
    Pavlidis, P., Gillis, J. Progress and challenges in the computational prediction of gene function using networks: 2012-2013 update F1000Res., 2 (2013),p. 230
    [139]
    Pavlopoulos, G.A., O'Donoghue, S.I., Satagopam, V.P. et al. Arena3D: visualization of biological networks in 3D BMC Syst. Biol., 2 (2008),p. 104
    [140]
    Peng, H., Long, F., Ding, C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans. Pattern Anal. Mach. Intell., 27 (2005),pp. 1226-1238
    [141]
    Pollack, J.R., Sorlie, T., Perou, C.M. et al. Microarray analysis reveals a major direct role of DNA copy number alteration in the transcriptional program of human breast tumors Proc. Natl. Acad. Sci. USA, 99 (2002),pp. 12963-12968
    [142]
    Prasad, T.K., Goel, R., Kandasamy, K. et al. Human protein reference database—2009 update Nucleic Acids Res., 37 (2009),pp. D767-D772
    [143]
    Pujana, M.A., Han, J.-D.J., Starita, L.M. et al. Network modeling links breast cancer susceptibility and centrosome dysfunction Nat. Genet., 39 (2007),pp. 1338-1349
    [144]
    Pyatnitskiy, M., Mazo, I., Shkrob, M. et al. Clustering gene expression regulators: new approach to disease subtyping PLoS One, 9 (2014),p. e84955
    [145]
    Qian, L., Zheng, H., Zhou, H. et al. PLoS One, 8 (2013),p. e58383
    [146]
    Ray, M., Ruan, J., Zhang, W. Variations in the transcriptome of Alzheimer's disease reveal molecular networks involved in cardiovascular diseases Genome Biol., 9 (2008),p. R148
    [147]
    Reilly, S.K., Yin, J., Ayoub, A.E. et al. Evolutionary changes in promoter and enhancer activity during human corticogenesis Science, 347 (2015),pp. 1155-1159
    [148]
    Ren, G., Liu, Z. NetCAD: a network analysis tool for coronary artery disease-associated PPI network Bioinformatics, 29 (2013),pp. 279-280
    [149]
    Rivera, C.G., Vakil, R., Bader, J.S. NeMo: network module identification in Cytoscape BMC Bioinformatics, 11 (2010),p. S61
    [150]
    Rual, J.-F., Venkatesan, K., Hao, T. et al. Towards a proteome-scale map of the human protein–protein interaction network Nature, 437 (2005),pp. 1173-1178
    [151]
    Ruan, J., Dean, A.K., Zhang, W. A general co-expression network-based approach to gene expression analysis: comparison and applications BMC Syst. Biol., 4 (2010),p. 8
    [152]
    Ruan, J., Zhang, W. Identifying network communities with a high resolution Phys. Rev. E Stat. Nonlin. Soft Matter Phys., 77 (2008),p. 016104
    [153]
    Rzhetsky, A., Wajngurt, D., Park, N. et al. Probing genetic overlap among complex human phenotypes Proc. Natl. Acad. Sci. USA, 104 (2007),pp. 11694-11699
    [154]
    Saha, A., Tan, A.C., Kang, J. Automatic context-specific subnetwork discovery from large interaction networks PLoS One, 9 (2014),p. e84227
    [155]
    Saito, R., Smoot, M.E., Ono, K. et al. A travel guide to cytoscape plugins Nat. Methods, 9 (2012),pp. 1069-1076
    [156]
    Salwinski, L., Miller, C.S., Smith, A.J. et al. The database of interacting proteins: 2004 update Nucleic Acids Res., 32 (2004),pp. D449-D451
    [157]
    Sarajlic, A., Janjic, V., Stojkovic, N. et al. Network topology reveals key cardiovascular disease genes PLoS One, 8 (2013),p. e71537
    [158]
    Sarajlic, A., Przulj, N. Survey of network-based approaches to research of cardiovascular diseases Biomed Res. Int., 2014 (2014),p. 527029
    [159]
    Schaefer, C.F., Anthony, K., Krupa, S. et al. PID: the pathway interaction database Nucleic Acids Res., 37 (2009),pp. D674-D679
    [160]
    Shannon, P., Markiel, A., Ozier, O. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks Genome Res., 13 (2003),pp. 2498-2504
    [161]
    Shi, Z., Wang, J., Zhang, B. NetGestalt: integrating multidimensional omics data over biological networks Nat. Methods, 10 (2013),pp. 597-598
    [162]
    Singh, R., Park, D., Xu, J. et al. Struct2Net: a web service to predict protein–protein interactions using a structure-based approach Nucleic Acids Res., 38 (2010),pp. W508-W515
    [163]
    Sivachenko, A.Y., Yuryev, A., Daraselia, N. et al. Molecular networks in microarray analysis J. Bioinform. Comput. Biol., 5 (2007),pp. 429-456
    [164]
    Stelzl, U., Worm, U., Lalowski, M. et al. A human protein–protein interaction network: a resource for annotating the proteome Cell, 122 (2005),pp. 957-968
    [165]
    Stöckel, D., Müller, O., Kehl, T. et al. NetworkTrail—a web service for identifying and visualizing deregulated subnetworks Bioinformatics, 29 (2013),pp. 1702-1703
    [166]
    Sun, J., Pan, Y., Feng, X. et al. iBIG: an integrative network tool for supporting human disease mechanism studies Genomics Proteomics Bioinformatics, 11 (2013),pp. 166-171
    [167]
    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),p. e1000662
    [168]
    Talwar, P., Silla, Y., Grover, S. et al. Genomic convergence and network analysis approach to identify candidate genes in Alzheimer's disease BMC Genomics, 15 (2014),p. 199
    [169]
    Taylor, I.W., Linding, R., Warde-Farley, D. et al. Dynamic modularity in protein interaction networks predicts breast cancer outcome Nat. Biotechnol., 27 (2009),pp. 199-204
    [170]
    Theocharidis, A., Van Dongen, S., Enright, A.J. et al. Network visualization and analysis of gene expression data using BioLayout Express3D Nat. Protoc., 4 (2009),pp. 1535-1550
    [171]
    Troyanskaya, O.G., Dolinski, K., Owen, A.B. et al. Proc. Natl. Acad. Sci. USA, 100 (2003),pp. 8348-8353
    [172]
    Tsiliki, G., Kossida, S. Fusion methodologies for biomedical data J. Proteom., 74 (2011),pp. 2774-2785
    [173]
    Turner, F.S., Clutterbuck, D.R., Semple, C.A. POCUS: mining genomic sequence annotation to predict disease genes Genome Biol., 4 (2003)
    [174]
    Tusher, V.G., Tibshirani, R., Chu, G. Significance analysis of microarrays applied to the ionizing radiation response Proc. Natl. Acad. Sci. USA, 98 (2001),pp. 5116-5121
    [175]
    Ulitsky, I., Maron-Katz, A., Shavit, S. et al. Expander: from expression microarrays to networks and functions Nat. Protoc., 5 (2010),pp. 303-322
    [176]
    Ummanni, R., Mundt, F., Pospisil, H. et al. Identification of clinically relevant protein targets in prostate cancer with 2D-DIGE coupled mass spectrometry and systems biology network platform PLoS One, 6 (2011),p. e16833
    [177]
    UniProt Consortium UniProt: a hub for protein information Nucleic Acids Res., 43 (2015),pp. D204-D212
    [178]
    van't Veer, L.J., Dai, H., van de Vijver, M.J. et al. Gene expression profiling predicts clinical outcome of breast cancer Nature, 415 (2002),pp. 530-536
    [179]
    Van den Akker, E.B., Verbruggen, B., Heijmans, B. et al. Integrating protein–protein interaction networks with gene–gene co-expression networks improves gene signatures for classifying breast cancer metastasis J. Integr. Bioinform., 8 (2011),p. 188
    [180]
    van Driel, M.A., Bruggeman, J., Vriend, G. et al. A text-mining analysis of the human phenome Eur. J. Hum. Genet., 14 (2006),pp. 535-542
    [181]
    Vandin, F., Upfal, E., Raphael, B.J. Algorithms for detecting significantly mutated pathways in cancer J. Comput. Biol., 18 (2011),pp. 507-522
    [182]
    Vanunu, O., Magger, O., Ruppin, E. et al. PLoS Comput. Biol., 6 (2010),p. e1000641
    [183]
    Vinh, N.X., Chetty, M., Coppel, R. et al. GlobalMIT: learning globally optimal dynamic bayesian network with the mutual information test criterion Bioinformatics, 27 (2011),pp. 2765-2766
    [184]
    Von Mering, C., Krause, R., Snel, B. et al. Comparative assessment of large-scale data sets of protein–protein interactions Nature, 417 (2002),pp. 399-403
    [185]
    Wang, Y., Thilmony, R., Gu, Y.Q. NetVenn: an integrated network analysis web platform for gene lists Nucleic Acids Res., 42 (2014),pp. W161-W166
    [186]
    Weile, J., James, K., Hallinan, J. et al. Bayesian integration of networks without gold standards Bioinformatics, 28 (2012),pp. 1495-1500
    [187]
    Whirl-Carrillo, M., McDonagh, E., Hebert, J. et al. Pharmacogenomics knowledge for personalized medicine Clin. Pharmacol. Ther., 92 (2012),pp. 414-417
    [188]
    Willsey, A.J., Sanders, S.J., Li, M. et al. Coexpression networks implicate human midfetal deep cortical projection neurons in the pathogenesis of autism Cell, 155 (2013),pp. 997-1007
    [189]
    Wu, G., Dawson, E., Duong, A. et al. ReactomeFIViz: a Cytoscape app for pathway and network-based data analysis F1000Res, 3 (2014),p. 146
    [190]
    Wu, G., Feng, X., Stein, L. Research A human functional protein interaction network and its application to cancer data analysis Genome Biol., 11 (2010),p. R53
    [191]
    Wu, G., Stein, L. A network module-based method for identifying cancer prognostic signatures Genome Biol., 13 (2012),p. R112
    [192]
    Wu, M.Y., Dai, D.Q., Zhang, X.F. et al. PLoS One, 8 (2013),p. e66256
    [193]
    Wu, X., Jiang, R., Zhang, M.Q. et al. Network-based global inference of human disease genes Mol. Syst. Biol., 4 (2008),p. 189
    [194]
    Xia, J., Benner, M.J., Hancock, R.E. NetworkAnalyst-integrative approaches for protein–protein interaction network analysis and visual exploration Nucleic Acids Res., 42 (2014),pp. W167-W174
    [195]
    Xu, J., Li, Y. Discovering disease-genes by topological features in human protein–protein interaction network Bioinformatics, 22 (2006),pp. 2800-2805
    [196]
    Yamanishi, Y., Araki, M., Gutteridge, A. et al. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces Bioinformatics, 24 (2008),pp. i232-i240
    [197]
    Yang, R., Bai, Y., Qin, Z. et al. EgoNet: identification of human disease ego-network modules BMC Genomics, 15 (2014),p. 314
    [198]
    Yu, W., He, L., Zhao, Y. et al. Dynamic protein-protein interaction subnetworks of lung cancer in cases with smoking history Chin. J. Cancer, 32 (2013),pp. 84-90
    [199]
    Yuryev, A., Kotelnikova, E., Daraselia, N. Ariadne's ChemEffect and Pathway Studio knowledge base Expert Opin. Drug Discov., 4 (2009),pp. 1307-1318
    [200]
    Zhang, S., Li, Q., Liu, J. et al. A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules Bioinformatics, 27 (2011),pp. i401-i409
    [201]
    Zhang, W., Ota, T., Shridhar, V. et al. Network-based survival analysis reveals subnetwork signatures for predicting outcomes of ovarian cancer treatment PLoS Comput. Biol., 9 (2013),p. e1002975
    [202]
    Zhang, X., Zhang, R., Jiang, Y. et al. The expanded human disease network combining protein–protein interaction information Eur. J. Hum. Genet., 19 (2011),pp. 783-788
    [203]
    Zhang, Y., De, S., Garner, J.R. et al. Systematic analysis, comparison, and integration of disease based human genetic association data and mouse genetic phenotypic information BMC Med. Genomics, 3 (2010),p. 1
    [204]
    Zhou, H., Pan, W., Shen, X. Penalized model-based clustering with unconstrained covariance matrices Electron. J. Stat., 3 (2009),p. 1473
    [205]
    Zhuang, L., Wu, Y., Han, J. et al. A network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma Biomed. Res. Int., 2014 (2014),p. 278956
    [206]
    Žitnik, M., Janjić, V., Larminie, C. et al. Discovering disease–disease associations by fusing systems-level molecular data Sci. Rep., 3 (2013),p. 3202
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    14. O’shea, R.. Understanding stroke with bayesian networks. Journal of Medical Artificial Intelligence, 2020, 3(March): 2. doi:10.21037/jmai.2019.09.01
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