1932

Abstract

Biomedical data scientists study many types of networks, ranging from those formed by neurons to those created by molecular interactions. People often criticize these networks as uninterpretable diagrams termed hairballs; however, here we show that molecular biological networks can be interpreted in several straightforward ways. First, we can break down a network into smaller components, focusing on individual pathways and modules. Second, we can compute global statistics describing the network as a whole. Third, we can compare networks. These comparisons can be within the same context (e.g., between two gene regulatory networks) or cross-disciplinary (e.g., between regulatory networks and governmental hierarchies). The latter comparisons can transfer a formalism, such as that for Markov chains, from one context to another or relate our intuitions in a familiar setting (e.g., social networks) to the relatively unfamiliar molecular context. Finally, key aspects of molecular networks are dynamics and evolution, i.e., how they evolve over time and how genetic variants affect them. By studying the relationships between variants in networks, we can begin to interpret many common diseases, such as cancer and heart disease.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-080917-013444
2018-07-20
2024-06-24
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/1/1/annurev-biodatasci-080917-013444.html?itemId=/content/journals/10.1146/annurev-biodatasci-080917-013444&mimeType=html&fmt=ahah

Literature Cited

  1. 1.  Hassabis D, Kumaran D, Summerfield C, Botvinick M 2017. Neuroscience-inspired artificial intelligence. Neuron 95:2245–58
    [Google Scholar]
  2. 2.  Leiserson MDM, Vandin F, Wu H-T, Dobson JR, Eldridge JV et al. 2015. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nat. Genet. 47:2106–14
    [Google Scholar]
  3. 3.  Vandin F, Upfal E, Raphael BJ 2011. Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol. 18:3507–22
    [Google Scholar]
  4. 4.  Pearl J 1982. Reverend Bayes on inference engines: a distributed hierarchical approach. Proc. AAAI Conf. Artif. Intell., 2nd, Pittsburgh, Pa., 18–20 Aug.133–36 Menlo Park, CA: AAAI
    [Google Scholar]
  5. 5.  Alipanahi B, Delong A, Weirauch MT, Frey BJ 2015. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33:8831–38
    [Google Scholar]
  6. 6.  Krizhevsky A, Sutskever I, Hinton GE 2012. ImageNet classification with deep convolutional neural networks. Proc. Int. Conf. Neural Inf. Process. Syst., 25th, Lake Tahoe, Nev., 3–6 Dec F Pereira, CJC Burges, L Bottou, KQ Weinberger 1097–105 Red Hook, NY: Curran Assoc.
    [Google Scholar]
  7. 7.  Yan K-K, Fang G, Bhardwaj N, Alexander RP, Gerstein M 2010. Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks. PNAS 107:209186–91
    [Google Scholar]
  8. 8.  Rieckmann JC, Geiger R, Hornburg D, Wolf T, Kveler K et al. 2017. Social network architecture of human immune cells unveiled by quantitative proteomics. Nat. Immunol. 18:5583–93
    [Google Scholar]
  9. 10.  Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan K-K et al. 2012. Architecture of the human regulatory network derived from ENCODE data. Nature 489:741491–100
    [Google Scholar]
  10. 9.  Khurana E, Fu Y, Colonna V, Mu XJ, Kang HM et al. 2013. Integrative annotation of variants from 1092 humans: application to cancer genomics. Science 342:61541235587
    [Google Scholar]
  11. 11.  Thiele I, Swainston N, Fleming RMT, Hoppe A, Sahoo S et al. 2013. A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31:5419–25
    [Google Scholar]
  12. 12.  Venkatakrishnan AJ, Deupi X, Lebon G, Tate CG, Schertler GF, Babu MM 2013. Molecular signatures of G-protein-coupled receptors. Nature 494:7436185–94
    [Google Scholar]
  13. 13.  Manglik A, Kim TH, Masureel M, Altenbach C, Yang Z et al. 2015. Structural insights into the dynamic process of β2-adrenergic receptor signaling. Cell 161:51101–11
    [Google Scholar]
  14. 14.  Rosenbaum DM, Cherezov V, Hanson MA, Rasmussen SGF, Thian FS et al. 2007. GPCR engineering yields high-resolution structural insights into β2-adrenergic receptor function. Science 318:58541266–73
    [Google Scholar]
  15. 15.  Luscombe NM, Babu MM, Yu H, Snyder M, Teichmann SA, Gerstein M 2004. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature 431:7006308–12
    [Google Scholar]
  16. 16.  Yosef N, Shalek AK, Gaublomme JT, Jin H, Lee Y et al. 2013. Dynamic regulatory network controlling TH17 cell differentiation. Nature 496:7446461–68
    [Google Scholar]
  17. 17.  Koren O, Goodrich JK, Cullender TC, Spor A, Laitinen K et al. 2012. Host remodeling of the gut microbiome and metabolic changes during pregnancy. Cell 150:3470–80
    [Google Scholar]
  18. 18.  Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E et al. 2015. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528:7581262–66
    [Google Scholar]
  19. 19.  Theriot CM, Koenigsknecht MJ, Carlson PE, Hatton GE, Nelson AM et al. 2014. Antibiotic-induced shifts in the mouse gut microbiome and metabolome increase susceptibility to Clostridium difficile infection. Nat. Commun. 5:3114
    [Google Scholar]
  20. 20.  Roadmap Epigenomics Consortium, Kundaje A, Meuleman W, Ernst J, Bilenky M et al. 2015. Integrative analysis of 111 reference human epigenomes. Nature 518:7539317–30
    [Google Scholar]
  21. 21.  Schadt EE 2009. Molecular networks as sensors and drivers of common human diseases. Nature 461:7261218–23
    [Google Scholar]
  22. 22.  Mangan S, Alon U 2003. Structure and function of the feed-forward loop network motif. PNAS 100:2111980–85
    [Google Scholar]
  23. 23.  Tu S, Pederson T, Weng Z 2013. Networking development by Boolean logic. Nucleus 4:289–91
    [Google Scholar]
  24. 24.  Peter IS, Faure E, Davidson EH 2012. Predictive computation of genomic logic processing functions in embryonic development. PNAS 109:4116434–42
    [Google Scholar]
  25. 25.  Moon TS, Lou C, Tamsir A, Stanton BC, Voigt CA 2012. Genetic programs constructed from layered logic gates in single cells. Nature 491:7423249–53
    [Google Scholar]
  26. 26.  Fenno LE, Mattis J, Ramakrishnan C, Hyun M, Lee SY et al. 2014. Targeting cells with single vectors using multiple-feature Boolean logic. Nat. Methods 11:7763–72
    [Google Scholar]
  27. 27.  Leeper NJ, Kullo IJ, Cooke JP 2012. Genetics of peripheral artery disease. Circulation 125:253220–28
    [Google Scholar]
  28. 28.  Sawa A, Snyder SH 2002. Schizophrenia: diverse approaches to a complex disease. Science 296:5568692–95
    [Google Scholar]
  29. 29.  Ripke S, Neale BM, Corvin A, Walters JTR, Farh K-H et al. 2014. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:7510421–27
    [Google Scholar]
  30. 30.  Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V et al. 2016. The genetic architecture of type 2 diabetes. Nature 536:761441–47
    [Google Scholar]
  31. 31.  Hoadley KA, Yau C, Wolf DM, Cherniack AD, Tamborero D et al. 2014. Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell 158:4929–44
    [Google Scholar]
  32. 32.  Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS et al. 2015. Prospective validation of a 21-gene expression assay in breast cancer. N. Engl. J. Med. 373:212005–14
    [Google Scholar]
  33. 33.  Mate SE, Kugelman JR, Nyenswah TG, Ladner JT, Wiley MR et al. 2015. Molecular evidence of sexual transmission of Ebola virus. N. Engl. J. Med. 373:252448–54
    [Google Scholar]
  34. 34.  Grubaugh ND, Ladner JT, Kraemer MUG, Dudas G, Tan AL et al. 2017. Genomic epidemiology reveals multiple introductions of Zika virus into the United States. Nature 546:7658401–5
    [Google Scholar]
  35. 35.  Faria NR, Azevedo RSS, Kraemer MUG, Souza R, Cunha MS et al. 2016. Zika virus in the Americas: early epidemiological and genetic findings. Science 352:6283345–49
    [Google Scholar]
  36. 36.  Collins FS, Morgan M, Patrinos A 2003. The Human Genome Project: lessons from large-scale biology. Science 300:5617286–90
    [Google Scholar]
  37. 37.  Chuang H-Y, Hofree M, Ideker T 2010. A decade of systems biology. Annu. Rev. Cell Dev. Biol. 26:721–44
    [Google Scholar]
  38. 38.  Monk J, Nogales J, Palsson BO 2014. Optimizing genome-scale network reconstructions. Nat. Biotechnol. 32:5447–52
    [Google Scholar]
  39. 39.  Magnúsdóttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E et al. 2017. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35:181–89
    [Google Scholar]
  40. 40.  Huttlin EL, Bruckner RJ, Paulo JA, Cannon JR, Ting L et al. 2017. Architecture of the human interactome defines protein communities and disease networks. Nature 545:7655505–9
    [Google Scholar]
  41. 41.  Marx V 2015. The DNA of a nation. Nature 524:7566503–5
    [Google Scholar]
  42. 42. Natl. Human Genome Res. Inst. 2016. NIH genome sequencing program targets the genomic bases of common, rare disease News Release, Jan. 14, updated Sept. 3 Natl. Inst. Health Washington, DC: https://www.genome.gov/27563453/
    [Google Scholar]
  43. 43.  Erlich Y 2015. A vision for ubiquitous sequencing. Genome Res 25:101411–16
    [Google Scholar]
  44. 44.  Shendure J, Aiden EL 2012. The expanding scope of DNA sequencing. Nat. Biotechnol. 30:111084–94
    [Google Scholar]
  45. 45.  Stephens ZD, Lee SY, Faghri F, Campbell RH, Zhai C et al. 2015. Big Data: astronomical or genomical?. PLOS Biol 13:7e1002195
    [Google Scholar]
  46. 46.  Yan K-K, Wang D, Sethi A, Muir P, Kitchen R et al. 2016. Cross-disciplinary network comparison: matchmaking between hairballs. Cell Syst 2:3147–57
    [Google Scholar]
  47. 47.  Pržulj N, Malod-Dognin N 2016. Network analytics in the age of big data. Science 353:6295123–24
    [Google Scholar]
  48. 48.  Benson AR, Gleich DF, Leskovec J 2016. Higher-order organization of complex networks. Science 353:6295163–66
    [Google Scholar]
  49. 49.  Schatz MC 2012. Computational thinking in the era of big data biology. Genome Biol 13:11177
    [Google Scholar]
  50. 50.  Wang B, Mezlini AM, Demir F, Fiume M, Tu Z et al. 2014. Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11:3333–37
    [Google Scholar]
  51. 51.  Drost M, Zonneveld JBM, van Dijk L, Morreau H, Tops CM et al. 2010. A cell-free assay for the functional analysis of variants of the mismatch repair protein MLH1. Hum. Mutat. 31:3247–53
    [Google Scholar]
  52. 52.  Letai A 2017. Functional precision cancer medicine—moving beyond pure genomics. Nat. Med. 23:91028–35
    [Google Scholar]
  53. 53.  Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K 2017. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D1D353–61
    [Google Scholar]
  54. 54.  Travers J, Milgram S 1969. An experimental study of the small world problem. Sociometry 32:4425–43
    [Google Scholar]
  55. 55.  Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH et al. 2005. A human protein-protein interaction network: a resource for annotating the proteome. Cell 122:6957–68
    [Google Scholar]
  56. 56.  Kim PM, Lu LJ, Xia Y, Gerstein MB 2006. Relating three-dimensional structures to protein networks provides evolutionary insights. Science 314:58071938–41
    [Google Scholar]
  57. 57.  Kim PM, Sboner A, Xia Y, Gerstein M 2008. The role of disorder in interaction networks: a structural analysis. Mol. Syst. Biol. 4:179
    [Google Scholar]
  58. 58.  Bhardwaj N, Abyzov A, Clarke D, Shou C, Gerstein MB 2011. Integration of protein motions with molecular networks reveals different mechanisms for permanent and transient interactions. Protein Sci 20:101745–54
    [Google Scholar]
  59. 59.  Kumar S, Clarke D, Gerstein M 2016. Localized structural frustration for evaluating the impact of sequence variants. Nucleic Acids Res 44:2110062–73
    [Google Scholar]
  60. 60.  Kim M-S, Kim J-R, Cho K-H 2010. Dynamic network rewiring determines temporal regulatory functions in Drosophila melanogaster development processes. BioEssays 32:6505–13
    [Google Scholar]
  61. 61.  Han J-DJ, Bertin N, Hao T, Goldberg DS, Berriz GF et al. 2004. Evidence for dynamically organized modularity in the yeast protein–protein interaction network. Nature 430:699588–93
    [Google Scholar]
  62. 62.  Nocedal I, Johnson AD 2015. How transcription networks evolve and produce biological novelty. Cold Spring Harb. Symp. Quant. Biol. 80:265–74
    [Google Scholar]
  63. 63.  Borneman AR, Gianoulis TA, Zhang ZD, Yu H, Rozowsky J et al. 2007. Divergence of transcription factor binding sites across related yeast species. Science 317:5839815–19
    [Google Scholar]
  64. 64.  Schmidt D, Wilson MD, Ballester B, Schwalie PC, Brown GD et al. 2010. Five-vertebrate ChIP-seq reveals the evolutionary dynamics of transcription factor binding. Science 328:59811036–40
    [Google Scholar]
  65. 65.  Shou C, Bhardwaj N, Lam HYK, Yan K-K, Kim PM et al. 2011. Measuring the evolutionary rewiring of biological networks. PLOS Comput. Biol. 7:1e1001050
    [Google Scholar]
  66. 66.  Kim J, Kim I, Han SK, Bowie JU, Kim S 2012. Network rewiring is an important mechanism of gene essentiality change. Sci. Rep. 2:1900
    [Google Scholar]
  67. 67.  Baumstark R, Hänzelmann S, Tsuru S, Schaerli Y, Francesconi M et al. 2015. The propagation of perturbations in rewired bacterial gene networks. Nat. Commun. 6:10105
    [Google Scholar]
  68. 68.  Creixell P, Schoof EM, Simpson CD, Longden J, Miller CJ et al. 2015. Kinome-wide decoding of network-attacking mutations rewiring cancer signaling. Cell 163:1202–17
    [Google Scholar]
  69. 69.  Creixell P, Palmeri A, Miller CJ, Lou HJ, Santini CC et al. 2015. Unmasking determinants of specificity in the human kinome. Cell 163:1187–201
    [Google Scholar]
  70. 70.  Grechkin M, Logsdon BA, Gentles AJ, Lee S-I 2016. Identifying network perturbation in cancer. PLOS Comput. Biol 12:5e1004888
    [Google Scholar]
  71. 71.  Bhardwaj N, Kim PM, Gerstein MB 2010. Rewiring of transcriptional regulatory networks: Hierarchy, rather than connectivity, better reflects the importance of regulators. Sci. Signal. 3:146ra79
    [Google Scholar]
  72. 72.  Wang D, Yan K-K, Sisu C, Cheng C, Rozowsky J et al. 2015. Loregic: a method to characterize the cooperative logic of regulatory factors. PLOS Comput. Biol. 11:4e1004132
    [Google Scholar]
  73. 73.  Prill RJ, Iglesias PA, Levchenko A 2005. Dynamic properties of network motifs contribute to biological network organization. PLOS Biol 3:11e343
    [Google Scholar]
  74. 74.  Rosenfeld N, Elowitz MB, Alon U 2002. Negative autoregulation speeds the response times of transcription networks. J. Mol. Biol. 323:5785–93
    [Google Scholar]
  75. 75.  Alon U 2007. Network motifs: theory and experimental approaches. Nat. Rev. Genet. 8:6450–61
    [Google Scholar]
  76. 76.  Ekman D, Light S, Björklund AK, Elofsson A 2006. What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae?. Genome Biol 7:6R45
    [Google Scholar]
  77. 77.  Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D et al. 2015. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res 43:D1D447–52
    [Google Scholar]
  78. 78.  Dandekar T, Snel B, Huynen M, Bork P 1998. Conservation of gene order: a fingerprint of proteins that physically interact. Trends Biochem. Sci. 23:9324–28
    [Google Scholar]
  79. 79.  Clauset A, Moore C, Newman MEJ 2008. Hierarchical structure and the prediction of missing links in networks. Nature 453:719198–101
    [Google Scholar]
  80. 80.  Jansen R, Yu H, Greenbaum D, Kluger Y, Krogan NJ et al. 2003. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302:5644449–53
    [Google Scholar]
  81. 81.  Zhang QC, Petrey D, Deng L, Qiang L, Shi Y et al. 2012. Structure-based prediction of protein–protein interactions on a genome-wide scale. Nature 490:7421556–60
    [Google Scholar]
  82. 82.  Zahiri J, Bozorgmehr J, Masoudi-Nejad A 2013. Computational prediction of protein–protein interaction networks: algorithms and resources. Curr. Genom. 14:6397–414
    [Google Scholar]
  83. 83.  Angermueller C, Pärnamaa T, Parts L, Stegle O 2016. Deep learning for computational biology. Mol. Syst. Biol. 12:7878
    [Google Scholar]
  84. 84.  Park Y, Kellis M 2015. Deep learning for regulatory genomics. Nat. Biotechnol. 33:8825–26
    [Google Scholar]
  85. 85.  Stergachis AB, Neph S, Sandstrom R, Haugen E, Reynolds AP et al. 2014. Conservation of trans-acting circuitry during mammalian regulatory evolution. Nature 515:7527365–70
    [Google Scholar]
  86. 86.  Lanchantin J, Singh R, Lin Z, Qi Y 2016. Deep Motif: visualizing genomic sequence classifications. arXiv:1605.01133 [cs.LG]
  87. 87.  Qin Q, Feng J 2017. Imputation for transcription factor binding predictions based on deep learning. PLOS Comput. Biol. 13:2e1005403
    [Google Scholar]
  88. 88.  Kelley DR, Snoek J, Rinn JL 2016. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res 26:7990–99
    [Google Scholar]
  89. 89.  Cowen L, Ideker T, Raphael BJ, Sharan R 2017. Network propagation: a universal amplifier of genetic associations. Nat. Rev. Genet. 18:9551–62
    [Google Scholar]
  90. 90.  Ruffalo M, Koyutürk M, Sharan R 2015. Network-based integration of disparate omic data to identify “silent players” in cancer. PLOS Comput. Biol. 11:12e1004595
    [Google Scholar]
  91. 91.  Erten S, Bebek G, Koyutürk M 2011. Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks. J. Comput. Biol. 18:111561–74
    [Google Scholar]
  92. 92.  Hofree M, Shen JP, Carter H, Gross A, Ideker T 2013. Network-based stratification of tumor mutations. Nat. Methods 10:111108–15
    [Google Scholar]
  93. 93.  Kim Y-A, Cho D-Y, Przytycka TM 2016. Understanding genotype-phenotype effects in cancer via network approaches. PLOS Comput. Biol. 12:3e1004747
    [Google Scholar]
  94. 94.  Mazza A, Klockmeier K, Wanker E, Sharan R 2016. An integer programming framework for inferring disease complexes from network data. Bioinformatics 32:12i271–77
    [Google Scholar]
  95. 95.  Nakka P, Raphael BJ, Ramachandran S 2016. Gene and network analysis of common variants reveals novel associations in multiple complex diseases. Genetics 204:2783–98
    [Google Scholar]
  96. 96.  Pastor-Satorras R, Smith E, Solé RV 2003. Evolving protein interaction networks through gene duplication. J. Theor. Biol. 222:2199–210
    [Google Scholar]
  97. 97.  Orsini C, Dankulov MM, Colomer-de-Simón P, Jamakovic A, Mahadevan P et al. 2015. Quantifying randomness in real networks. Nat. Commun. 6:8627
    [Google Scholar]
  98. 98.  Piel FB, Steinberg MH, Rees DC 2017. Sickle cell disease. N. Engl. J. Med. 376:161561–73
    [Google Scholar]
  99. 99.  Dempfle A, Scherag A, Hein R, Beckmann L, Chang-Claude J, Schäfer H 2008. Gene-environment interactions for complex traits: definitions, methodological requirements and challenges. Eur. J. Hum. Genet. 16:101164–72
    [Google Scholar]
  100. 100.  Wood AR, Esko T, Yang J, Vedantam S, Pers TH et al. 2014. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46:111173–86
    [Google Scholar]
  101. 101.  Leiserson MD, Eldridge JV, Ramachandran S, Raphael BJ 2013. Network analysis of GWAS data. Curr. Opin. Genet. Dev. 23:6602–10
    [Google Scholar]
  102. 102.  Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL et al. 2005. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS 102:4315545–50
    [Google Scholar]
  103. 103.  Pinto D, Delaby E, Merico D, Barbosa M, Merikangas A et al. 2014. Convergence of genes and cellular pathways dysregulated in autism spectrum disorders. Am. J. Hum. Genet. 94:5677–94
    [Google Scholar]
  104. 104.  Krishnan A, Zhang R, Yao V, Theesfeld CL, Wong AK et al. 2016. Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nat. Neurosci. 19:111454–62
    [Google Scholar]
  105. 105.  Mootha VK, Lindgren CM, Eriksson K-F, Subramanian A, Sihag S et al. 2003. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34:3267–73
    [Google Scholar]
  106. 106.  Ramachandran S, Karp PH, Jiang P, Ostedgaard LS, Walz AE et al. 2012. A microRNA network regulates expression and biosynthesis of wild-type and ΔF508 mutant cystic fibrosis transmembrane conductance regulator. PNAS 109:3313362–67
    [Google Scholar]
  107. 107.  Guggino WB, Stanton BA 2006. New insights into cystic fibrosis: molecular switches that regulate CFTR. Nat. Rev. Mol. Cell Biol. 7:6426–36
    [Google Scholar]
  108. 108.  Gu Y, Harley ITW, Henderson LB, Aronow BJ, Vietor I et al. 2009. Identification of IFRD1 as a modifier gene for cystic fibrosis lung disease. Nature 458:72411039–42
    [Google Scholar]
  109. 109.  Hemani G, Shakhbazov K, Westra H-J, Esko T, Henders AK et al. 2014. Detection and replication of epistasis influencing transcription in humans. Nature 508:7495249–53
    [Google Scholar]
  110. 110.  Carlborg Ö, Haley CS 2004. Epistasis: too often neglected in complex trait studies?. Nat. Rev. Genet. 5:8618–25
    [Google Scholar]
  111. 111.  Costanzo M, VanderSluis B, Koch EN, Baryshnikova A, Pons C et al. 2016. A global genetic interaction network maps a wiring diagram of cellular function. Science 353:6306aaf1420
    [Google Scholar]
  112. 112. Cross-Disord. Group Psychiatr. Genom. Consort. 2013. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 381:98751371–79
    [Google Scholar]
  113. 113.  Goh K-I, Cusick ME, Valle D, Childs B, Vidal M, Barabasi A-L 2007. The human disease network. PNAS 104:218685–90
    [Google Scholar]
  114. 114.  Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R 2013. Structure and dynamics of molecular networks: a novel paradigm of drug discovery. Pharmacol. Ther. 138:3333–408
    [Google Scholar]
  115. 115.  Mohamed S, Johnson GR, Chen P, Hicks PB, Davis LL et al. 2017. Effect of antidepressant switching versus augmentation on remission among patients with major depressive disorder unresponsive to antidepressant treatment. JAMA 318:2132–45
    [Google Scholar]
  116. 116.  Lee MJ, Ye AS, Gardino AK, Heijink AM, Sorger PK et al. 2012. Sequential application of anticancer drugs enhances cell death by rewiring apoptotic signaling networks. Cell 149:4780–94
    [Google Scholar]
  117. 117.  Keith CT, Borisy AA, Stockwell BR 2005. Innovation: multicomponent therapeutics for networked systems. Nat. Rev. Drug Discov. 4:171–78
    [Google Scholar]
  118. 118.  Günthard HF, Saag MS, Benson CA, del Rio C, Eron JJ et al. 2016. Antiretroviral drugs for treatment and prevention of HIV infection in adults. JAMA 316:2191–210
    [Google Scholar]
  119. 119.  Saxena P, Charpin-El Hamri G, Folcher M, Zulewski H, Fussenegger M 2016. Synthetic gene network restoring endogenous pituitary-thyroid feedback control in experimental Graves’ disease. PNAS 113:51244–49
    [Google Scholar]
  120. 120.  Koboldt DC, Fulton RS, McLellan MD, Schmidt H, Kalicki-Veizer J et al. 2012. Comprehensive molecular portraits of human breast tumours. Nature 490:741861–70
    [Google Scholar]
  121. 121.  Collisson EA, Campbell JD, Brooks AN, Berger AH, Lee W et al. 2014. Comprehensive molecular profiling of lung adenocarcinoma. Nature 511:7511543–50
    [Google Scholar]
  122. 122.  Cancer Genome Atlas Res Netw, Linehan WM, Spellman PT, Ricketts CJ, Creighton CJ et al. 2016. Comprehensive molecular characterization of papillary renal-cell carcinoma. N. Engl. J. Med. 374:2135–45
    [Google Scholar]
  123. 123.  Varadhachary GR, Raber MN 2014. Cancer of unknown primary site. N. Engl. J. Med. 371:8757–65
    [Google Scholar]
  124. 124.  Khurana E, Fu Y, Chen J, Gerstein M 2013. Interpretation of genomic variants using a unified biological network approach. PLOS Comput. Biol. 9:3e1002886
    [Google Scholar]
  125. 125.  Fu Y, Liu Z, Lou S, Bedford J, Mu XJ et al. 2014. FunSeq2: a framework for prioritizing noncoding regulatory variants in cancer. Genome Biol 15:10480
    [Google Scholar]
  126. 126.  Jiang P, Freedman ML, Liu JS, Liu XS 2015. Inference of transcriptional regulation in cancers. PNAS 112:257731–36
    [Google Scholar]
  127. 127.  Falco MM, Bleda M, Carbonell-Caballero J, Dopazo J 2016. The pan-cancer pathological regulatory landscape. Sci. Rep. 6:139709
    [Google Scholar]
  128. 128.  Bashashati A, Haffari G, Ding J, Ha G, Lui K et al. 2012. DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer. Genome Biol 13:12R124
    [Google Scholar]
  129. 129.  Jia P, Zhao Z 2014. VarWalker: personalized mutation network analysis of putative cancer genes from next-generation sequencing data. PLOS Comput. Biol. 10:2e1003460
    [Google Scholar]
  130. 130.  Creighton CJ, Morgan M, Gunaratne PH, Wheeler DA, Gibbs RA et al. 2013. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature 499:745643–49
    [Google Scholar]
  131. 131.  Sharma P, Allison JP 2015. The future of immune checkpoint therapy. Science 348:623056–61
    [Google Scholar]
  132. 132.  Burr ML, Sparbier CE, Chan Y-C, Williamson JC, Woods K et al. 2017. CMTM6 maintains the expression of PD-L1 and regulates anti-tumour immunity. Nature 549:7670101–5
    [Google Scholar]
  133. 133.  Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W et al. 2016. Mutations associated with acquired resistance to PD-1 blockade in melanoma. N. Engl. J. Med. 375:9819–29
    [Google Scholar]
  134. 134.  Yan K-K, Fang G, Bhardwaj N, Alexander RP, Gerstein M 2010. Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks. PNAS 107:209186–91
    [Google Scholar]
  135. 135.  Navlakha S, He X, Faloutsos C, Bar-Joseph Z 2014. Topological properties of robust biological and computational networks. J. R. Soc. Interface 11:9620140283
    [Google Scholar]
  136. 136.  Bird L 2017. Immune regulation: immune cell social networks. Nat. Rev. Immunol. 17:4216
    [Google Scholar]
  137. 137.  Bergthaler A, Menche J 2017. The immune system as a social network. Nat. Immunol. 18:5481–82
    [Google Scholar]
  138. 138.  Guimerà R, Sales-Pardo M, Amaral LAN 2007. Classes of complex networks defined by role-to-role connectivity profiles. Nat. Phys. 3:163–69
    [Google Scholar]
  139. 139.  Guimerà R, Mossa S, Turtschi A, Amaral LAN 2005. The worldwide air transportation network: anomalous centrality, community structure, and cities’ global roles. PNAS 102:227794–99
    [Google Scholar]
  140. 140.  Guimerà R, Nunes Amaral LA 2005. Functional cartography of complex metabolic networks. Nature 433:7028895–900
    [Google Scholar]
  141. 141.  Colizza V, Flammini A, Serrano MA, Vespignani A 2006. Detecting rich-club ordering in complex networks. Nat. Phys. 2:2110–15
    [Google Scholar]
  142. 142.  Pržulj N, Corneil DG, Jurisica I 2004. Modeling interactome: scale-free or geometric?. Bioinformatics 20:183508–15
    [Google Scholar]
  143. 143.  Wu Z, Menichetti G, Rahmede C, Bianconi G 2015. Emergent complex network geometry. Sci. Rep. 5:110073
    [Google Scholar]
  144. 144.  Tero A, Takagi S, Saigusa T, Ito K, Bebber DP et al. 2010. Rules for biologically inspired adaptive network design. Science 327:5964439–42
    [Google Scholar]
  145. 145.  Newman MEJ 2006. Modularity and community structure in networks. PNAS 103:238577–82
    [Google Scholar]
  146. 146.  Freeman LC 1977. A set of measures of centrality based on betweenness. Sociometry 40:135–41
    [Google Scholar]
  147. 147.  Borgatti SP, Everett MG 2006. A graph-theoretic perspective on centrality. Soc. Netw. 28:4466–84
    [Google Scholar]
  148. 148.  Newman MEJ, Girvan M 2004. Finding and evaluating community structure in networks. Phys. Rev. E 69:226113
    [Google Scholar]
  149. 149.  Tibshirani R, Walther G, Hastie T 2001. Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. B 63:2411–23
    [Google Scholar]
  150. 150.  Clauset A, Newman MEJ, Moore C 2004. Finding community structure in very large networks. Phys. Rev. E 70:666111
    [Google Scholar]
  151. 151.  Radicchi F, Castellano C, Cecconi F, Loreto V, Parisi D 2004. Defining and identifying communities in networks. PNAS 101:92658–63
    [Google Scholar]
  152. 152.  Rosvall M, Bergstrom CT 2007. An information-theoretic framework for resolving community structure in complex networks. PNAS 104:187327–31
    [Google Scholar]
  153. 153.  Palla G, Derényi I, Farkas I, Vicsek T 2005. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435:7043814–18
    [Google Scholar]
  154. 154.  Donetti L, Muñoz MA 2004. Detecting network communities: a new systematic and efficient algorithm. J. Stat. Mech. 2004:10P10012
    [Google Scholar]
  155. 155.  Ronhovde P, Nussinov Z 2009. Multiresolution community detection for megascale networks by information-based replica correlations. Phys. Rev. E 80:116109
    [Google Scholar]
  156. 156.  Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E 2008. Fast unfolding of communities in large networks. J. Stat. Mech. 2008:10P10008
    [Google Scholar]
  157. 157.  Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P 2008. Molecular Biology of The Cell New York: Garland Sci. , 5th ed..
    [Google Scholar]
  158. 158.  Gnatt AL, Cramer P, Fu J, Bushnell DA, Kornberg RD 2001. Structural basis of transcription: an RNA polymerase II elongation complex at 3.3 Å resolution. Science 292:55231876–82
    [Google Scholar]
  159. 159.  Rose AS, Hildebrand PW 2015. NGL Viewer: a web application for molecular visualization. Nucleic Acids Res 43:W1W576–79
    [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-080917-013444
Loading
/content/journals/10.1146/annurev-biodatasci-080917-013444
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error