Understanding Genotype-Phenotype Effects in Cancer via Network Approaches

PLoS Computational Biology

Volumn 12, Issue 3, 2016, Pages

  Kim, Yoo Ah ^a   Cho, Dong Yeon ^a   Przytycka, Teresa M ^a  

^a NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GENES;

CANCER PATIENTS; DISEASE PHENOTYPES; DRUG TARGETS; GENOMIC PROFILES; MUTATED GENES; NETWORK-CENTRIC APPROACH; PATIENT VARIATION;

DISEASES;

ARTICLE; CANCER GENETICS; CANCER PATIENT; CARCINOGENESIS; DISEASE CLASSIFICATION; DISEASE COURSE; GENE MUTATION; GENE REGULATORY NETWORK; GENETIC DISORDER; GENETIC HETEROGENEITY; GENETIC VARIABILITY; GENOMICS; GENOTYPE PHENOTYPE CORRELATION; HUMAN; MALIGNANT NEOPLASTIC DISEASE; MOLECULAR GENETICS; MOLECULAR PATHOLOGY; PERSONALIZED MEDICINE; ANIMAL; BIOLOGICAL MODEL; COMPUTER SIMULATION; GENETIC PREDISPOSITION; GENETICS; GENOTYPE; METABOLISM; NEOPLASM; PATHOPHYSIOLOGY; PHENOTYPE; PROCEDURES; PROTEIN ANALYSIS; SIGNAL TRANSDUCTION;

TUMOR PROTEIN;

ANIMALS; COMPUTER SIMULATION; GENETIC PREDISPOSITION TO DISEASE; GENOTYPE; HUMANS; MODELS, BIOLOGICAL; NEOPLASM PROTEINS; NEOPLASMS; PHENOTYPE; PROTEIN INTERACTION MAPPING; SIGNAL TRANSDUCTION;

EID: 84962106951     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1004747     Document Type: Article

References (86)

1. Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature. 2005;437(7062):1173–8. doi: 10.1038/nature0420916189514.
2. Stelzl U, Worm U, Lalowski M, Haenig C, Brembeck FH, Goehler H, et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell. 2005;122(6):957–68. doi: 10.1016/j.cell.2005.08.02916169070.
3. Das J, Yu H, HINT: High-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol. 2012;6:92. doi: 10.1186/1752-0509-6-9222846459; PubMed Central PMCID: PMC3483187.
4. Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM, Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome research. 2011;21(7):1109–21. doi: 10.1101/gr.118992.11021536720; PubMed Central PMCID: PMC3129253.
5. Snel B, Lehmann G, Bork P, Huynen MA, STRING: a web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic acids research. 2000;28(18):3442–4. 10982861; PubMed Central PMCID: PMC110752.
6. Roque FS, Jensen PB, Schmock H, Dalgaard M, Andreatta M, Hansen T, et al. Using electronic patient records to discover disease correlations and stratify patient cohorts. PLoS Comput Biol. 2011;7(8):e1002141. doi: 10.1371/journal.pcbi.100214121901084; PubMed Central PMCID: PMC3161904.
7. Cho DY, Przytycka TM, Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model. Nucleic acids research. 2013;41(17):8011–20. doi: 10.1093/nar/gkt57723821670; PubMed Central PMCID: PMC3783162.
8. Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, Brudno M, et al. Similarity network fusion for aggregating data types on a genomic scale. Nature methods. 2014;11(3):333–7. doi: 10.1038/nmeth.281024464287.
9. Suthram S, Dudley JT, Chiang AP, Chen R, Hastie TJ, Butte AJ, Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol. 2010;6(2):e1000662. Epub 2010/02/09. doi: 10.1371/journal.pcbi.100066220140234; PubMed Central PMCID: PMC2816673.
10. Barabasi AL, Gulbahce N, Loscalzo J, Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12(1):56–68. doi: 10.1038/nrg291821164525; PubMed Central PMCID: PMC3140052.
11. Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, Loscalzo J, et al. Disease networks. Uncovering disease-disease relationships through the incomplete interactome. Science. 2015;347(6224):1257601. doi: 10.1126/science.125760125700523; PubMed Central PMCID: PMC4435741.
12. Wu X, Jiang R, Zhang MQ, Li S, Network-based global inference of human disease genes. Molecular systems biology. 2008;4:189. 18463613. doi: 10.1038/msb.2008.27
13. Xie M, Xu Y, Zhang Y, Hwang T, Kuang R, Network-based Phenome-Genome Association Prediction by Bi-Random Walk. PLoS ONE. 2015;10(5):e0125138. Epub 2015/05/02. doi: 10.1371/journal.pone.012513825933025; PubMed Central PMCID: PMC4416812.
14. Yildirim MA, Goh KI, Cusick ME, Barabasi AL, Vidal M, Drug-target network. Nature biotechnology. 2007;25(10):1119–26. doi: 10.1038/nbt133817921997.
15. Chuang HY, Lee E, Liu YT, Lee D, Ideker T, Network-based classification of breast cancer metastasis. Molecular systems biology. 2007;3:140. 17940530.
16. Muller FJ, Laurent LC, Kostka D, Ulitsky I, Williams R, Lu C, et al. Regulatory networks define phenotypic classes of human stem cell lines. Nature. 2008;455(7211):401–5. Epub 2008/08/30. doi: 10.1038/nature0721318724358; PubMed Central PMCID: PMC2637443.
17. Mani KM, Lefebvre C, Wang K, Lim WK, Basso K, Dalla-Favera R, et al. A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Molecular systems biology. 2008;4:169. Epub 2008/02/16. doi: 10.1038/msb.2008.218277385; PubMed Central PMCID: PMC2267731.
18. Xue H, Xian B, Dong D, Xia K, Zhu S, Zhang Z, et al. A modular network model of aging. Molecular systems biology. 2007;3:147. Epub 2007/12/07. doi: 10.1038/msb410018918059442; PubMed Central PMCID: PMC2174624.
19. Xia K, Xue H, Dong D, Zhu S, Wang J, Zhang Q, et al. Identification of the proliferation/differentiation switch in the cellular network of multicellular organisms. PLoS Comput Biol. 2006;2(11):e145. Epub 2006/12/15. doi: 06-PLCB-RA-0178R2 [pii] doi: 10.1371/journal.pcbi.002014517166053; PubMed Central PMCID: PMC1664705.
20. Patel VN, Gokulrangan G, Chowdhury SA, Chen Y, Sloan AE, Koyuturk M, et al. Network signatures of survival in glioblastoma multiforme. PLoS Comput Biol. 2013;9(9):e1003237. doi: 10.1371/journal.pcbi.100323724068912; PubMed Central PMCID: PMC3777929.
21. Kim YA, Wuchty S, Przytycka TM, Berger B, Simultaneous Identification of Causal Genes and Dys-Regulated Pathways in Complex Diseases. In: Research in Computational Molecular Biology (RECOMB). Springer-VerlagBerlin Heidelberg, 2010.
22. Wilentzik R, Gat-Viks I, A statistical framework for revealing signaling pathways perturbed by DNA variants. Nucleic acids research. 2015;43(11):e74. doi: 10.1093/nar/gkv20325765646; PubMed Central PMCID: PMC4477643.
23. Vandin F, Upfal E, Raphael BJ, Algorithms for detecting significantly mutated pathways in cancer. J Comput Biol. 2011;18(3):507–22. Epub 2011/03/10. doi: 10.1089/cmb.2010.026521385051.
24. Gilman SR, Iossifov I, Levy D, Ronemus M, Wigler M, Vitkup D, Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron. 2011;70(5):898–907. Epub 2011/06/11. doi: S0896-6273(11)00439-9 [pii] doi: 10.1016/j.neuron.2011.05.02121658583.
25. Levy D, Ronemus M, Yamrom B, Lee YH, Leotta A, Kendall J, et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron. 2011;70(5):886–97. Epub 2011/06/11. doi: S0896-6273(11)00396-5 [pii] doi: 10.1016/j.neuron.2011.05.01521658582.
26. Rossin EJ, Lage K, Raychaudhuri S, Xavier RJ, Tatar D, Benita Y, et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet. 2011;7(1):e1001273. Epub 2011/01/21. doi: 10.1371/journal.pgen.100127321249183; PubMed Central PMCID: PMC3020935.
27. Cerami E, Demir E, Schultz N, Taylor BS, Sander C, Automated network analysis identifies core pathways in glioblastoma. PLoS ONE. 2010;5(2):e8918. doi: 10.1371/journal.pone.000891820169195; PubMed Central PMCID: PMC2820542.
28. Hahn WC, Weinberg RA, Modelling the molecular circuitry of cancer. Nat Rev Cancer. 2002;2(5):331–41. doi: 10.1038/nrc79512044009.
29. Vogelstein B, Kinzler KW, Cancer genes and the pathways they control. Nat Med. 2004;10(8):789–99. doi: 10.1038/nm108715286780.
30. Vogelstein B, Papadopoulos N, Velculescu VE, Zhou S, Diaz LA, Jr.Kinzler KW, Cancer genome landscapes. Science. 2013;339(6127):1546–58. doi: 10.1126/science.123512223539594; PubMed Central PMCID: PMC3749880.
31. Kohler S, Bauer S, Horn D, Robinson PN, Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet. 2008;82(4):949–58. 18371930. doi: 10.1016/j.ajhg.2008.02.013
32. Vanunu O, Magger O, Ruppin E, Shlomi T, Sharan R, Associating genes and protein complexes with disease via network propagation. PLoS Comput Biol. 2010;6(1):e1000641. Epub 2010/01/22. doi: 10.1371/journal.pcbi.100064120090828; PubMed Central PMCID: PMC2797085.
33. Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M, Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics. 2005;21Suppl 1:i302–10. 15961472.
34. Stojmirovic A, Yu YK, Information flow in interaction networks. J Comput Biol. 2007;14(8):1115–43. 17985991.
35. Cho H, Berger B, Peng J, Przytycka T, Diffusion Component Analysis: Unraveling Functional Topology in Biological Networks. In: Research in Computational Molecular Biology (RECOMB). Springer International PublishingSwitzerland, 2015.
36. Hamaneh MB, Yu YK, Relating diseases by integrating gene associations and information flow through protein interaction network. PLoS ONE. 2014;9(10):e110936. doi: 10.1371/journal.pone.011093625360770; PubMed Central PMCID: PMC4216010.
37. Zotenko E, Mestre J, O'Leary DP, Przytycka TM, Why do hubs in the yeast protein interaction network tend to be essential: reexamining the connection between the network topology and essentiality. PLoS Comput Biol. 2008;4(8):e1000140. 18670624. doi: 10.1371/journal.pcbi.1000140
38. Newman M, A measure of betweenness centrality based on random walks. Social Networks. 200527(1):39–54.
39. Kim YA, Wuchty S, Przytycka TM, Identifying causal genes and dysregulated pathways in complex diseases. PLoS Comput Biol. 2011;7(3):e1001095. doi: 10.1371/journal.pcbi.100109521390271; PubMed Central PMCID: PMC3048384.
40. Tu Z, Wang L, Arbeitman MN, Chen T, Sun F, An integrative approach for causal gene identification and gene regulatory pathway inference. Bioinformatics. 2006;22(14):e489–96. 16873511.
41. Suthram S, Beyer A, Karp RM, Eldar Y, Ideker T, eQED: an efficient method for interpreting eQTL associations using protein networks. Mol Syst Biol. 2008;4:162. 18319721. doi: 10.1038/msb.2008.4
42. Kim YA, Przytycki JH, Wuchty S, Przytycka TM, Modeling information flow in biological networks. Phys Biol. 2011;8(3):035012. Epub 2011/05/17. doi: 10.1088/1478-3975/8/3/03501221572171; PubMed Central PMCID: PMC3148109.
43. Yeger-Lotem E, Riva L, Su LJ, Gitler AD, Cashikar AG, King OD, et al. Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nature genetics. 2009;41(3):316–23. 19234470. doi: 10.1038/ng.337
44. Leiserson MD, Vandin F, Wu HT, Dobson JR, Eldridge JV, Thomas JL, et al. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes. Nature genetics. 2015;47(2):106–14. Epub 2014/12/17. doi: 10.1038/ng.316825501392; PubMed Central PMCID: PMC4444046.
45. Hofree M, Shen JP, Carter H, Gross A, Ideker T, Network-based stratification of tumor mutations. Nature methods. 2013;10(11):1108–15. Epub 2013/09/17. doi: 10.1038/nmeth.265124037242; PubMed Central PMCID: PMC3866081.
46. Kim YA, Salari R, Wuchty S, Przytycka TM, Module cover—a new approach to genotype-phenotype studies. Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing. 2013:135–46. Epub 2013/02/21. 23424119; PubMed Central PMCID: PMC3595055.
47. Kim YA, Cho DY, Dao P, Przytycka TM, MEMCover: integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types. Bioinformatics. 2015;31(12):i284–i292. Epub 2015/06/15. doi: 10.1093/bioinformatics/btv24726072494; PubMed Central PMCID: PMC4481701.
48. Ulitsky I, Karp R, Shamir R, Vingrom M, Wong L, Detecting Disease-Specific Dysregulated Pathways Via Analysis of Clinical Expression Profiles. In: Research in Computational Molecular Biology (RECOMB); Springer-VerlagBerlin Heidelberg, 2008.
49. Chowdhury SA, Koyuturk M, Identification of coordinately dysregulated subnetworks in complex phenotypes. Pacific Symposium on Biocomputing. 2010:133–44. Epub 2009/11/13. doi: 9789814295291_0016 [pii]. 19908366.
50. Lu S, Lu KN, Cheng S, Ma X, Nystrom N, Lu X, Identifying driver genomic alterations in cancers by searching minimum-weight, mutually exclusive sets. PLoS Comput Biol. 2015; 11(8):e1004257. doi: 10.1371/journal.pcbi.100425726317392
51. Ulitsky I, Krishnamurthy A, Karp RM, Shamir R, DEGAS: de novo discovery of dysregulated pathways in human diseases. PLoS ONE. 2010;5(10):e13367. doi: 10.1371/journal.pone.001336720976054; PubMed Central PMCID: PMC2957424.
52. Bailly-Bechet M, Borgs C, Braunstein A, Chayes J, Dagkessamanskaia A, Francois JM, et al. Finding undetected protein associations in cell signaling by belief propagation. Proc Natl Acad Sci U S A. 2011;108(2):882–7. Epub 2010/12/29. doi: 1004751108 [pii] doi: 10.1073/pnas.100475110821187432; PubMed Central PMCID: PMC3021011.
53. Tuncbag N, McCallum S, Huang SS, Fraenkel E, SteinerNet: a web server for integrating 'omic' data to discover hidden components of response pathways. Nucleic acids research. 2012;40(Web Server issue):W505–9. Epub 2012/05/29. doi: gks445 [pii] doi: 10.1093/nar/gks44522638579; PubMed Central PMCID: PMC3394335.
54. Huang SS, Fraenkel E, Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Science signaling. 2009;2(81):ra40. doi: 10.1126/scisignal.200035019638617; PubMed Central PMCID: PMC2889494.
55. Nurcan Tuncbag AB, Pagnani Andrea, Huang Shao-Shan Carol, Chayes Jennifer, Borgs Christian, Zecchina Riccardo, Fraenkel Ernest, Chor E, Simultaneous reconstruction of multiple signaling pathways via the prize-collecting Steiner forest problem. In: Research in Computational Molecular Biology (RECOMB); Springer-VerlagBerlin Heidelberg, 2012.
56. Gitter A, Braunstein A, Pagnani A, Baldassi C, Borgs C, Chayes J, et al. Sharing information to reconstruct patient-specific pathways in heterogeneous diseases. Pacific Symposium on Biocomputing. 2014:39–50. 24297532; PubMed Central PMCID: PMC3910098.
57. Szczurek E, Misra N, Vingron M, Synthetic sickness or lethality points at candidate combination therapy targets in glioblastoma. Int J Cancer. 2013;133(9):2123–32. doi: 10.1002/ijc.2823523629686.
58. Jerby-Arnon L, Pfetzer N, Waldman YY, McGarry L, James D, Shanks E, et al. Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality. Cell. 2014;158(5):1199–209. doi: 10.1016/j.cell.2014.07.02725171417.
59. Ciriello G, Cerami E, Aksoy BA, Sander C, Schultz N, Baxevanis Andreas D, Using MEMo to discover mutual exclusivity modules in cancer. Current protocols in bioinformatics / editoral board, et al]. 2013;Chapter 8:Unit 8 17. Epub 2013/03/19. doi: 10.1002/0471250953.bi0817s4123504936.
60. Ciriello G, Cerami E, Sander C, Schultz N, Mutual exclusivity analysis identifies oncogenic network modules. Genome research. 2012;22(2):398–406. Epub 2011/09/13. doi: 10.1101/gr.125567.11121908773; PubMed Central PMCID: PMC3266046.
61. Babur O, Gonen M, Aksoy BA, Schultz N, Ciriello G, Sander C, et al. Systematic identification of cancer driving signaling pathways based on mutual exclusivity of genomic alterations. Genome biology. 2015;16:45. Epub 2015/04/19. doi: 10.1186/s13059-015-0612-625887147; PubMed Central PMCID: PMC4381444.
62. Miller CA, Settle SH, Sulman EP, Aldape KD, Milosavljevic A, Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors. BMC Med Genomics. 2011;4:34. doi: 10.1186/1755-8794-4-3421489305; PubMed Central PMCID: PMC3102606.
63. Vandin F, Upfal E, Raphael BJ, De novo discovery of mutated driver pathways in cancer. Genome research. 2012;22(2):375–85. Epub 2011/06/10. doi: gr.120477.111 [pii] doi: 10.1101/gr.120477.11121653252; PubMed Central PMCID: PMC3266044.
64. Zhao J, Zhang S, Wu LY, Zhang XS, Efficient methods for identifying mutated driver pathways in cancer. Bioinformatics. 2012;28(22):2940–7. doi: 10.1093/bioinformatics/bts56422982574.
65. Leiserson MD, Wu HT, Vandin F, Raphael BJ, CoMEt: a statistical approach to identify combinations of mutually exclusive alterations in cancer. Genome biology. 2015;16:160. doi: 10.1186/s13059-015-0700-726253137; PubMed Central PMCID: PMC4531541.
66. Szczurek E, Beerenwinkel N, Modeling mutual exclusivity of cancer mutations. PLoS Comput Biol. 2014;10(3):e1003503. Epub 2014/03/29. doi: 10.1371/journal.pcbi.100350324675718; PubMed Central PMCID: PMC3967923.
67. Leiserson MD, Blokh D, Sharan R, Raphael BJ, Simultaneous identification of multiple driver pathways in cancer. PLoS Comput Biol. 2013;9(5):e1003054. doi: 10.1371/journal.pcbi.100305423717195; PubMed Central PMCID: PMC3662702.
68. Zhang J, Wu LY, Zhang XS, Zhang S, Discovery of co-occurring driver pathways in cancer. BMC bioinformatics. 2014;15:271. doi: 10.1186/1471-2105-15-27125106096; PubMed Central PMCID: PMC4133618.
69. Constantinescu S, Szczurek E, Mohammadi P, Rahnenfuhrer J, Beerenwinkel N, TiMEx: a waiting time model for mutually exclusive cancer alterations. Bioinformatics. 2015. doi: 10.1093/bioinformatics/btv40026163509.
70. Cancer Genome Atlas Research NetworkWeinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nature genetics. 2013;45(10):1113–20. doi: 10.1038/ng.276424071849; PubMed Central PMCID: PMC3919969.
71. Park S, Lehner B, Cancer type-dependent genetic interactions between cancer driver alterations indicate plasticity of epistasis across cell types. Molecular systems biology. 2015;11(7):824. doi: 10.15252/msb.2015610226227665.
72. Blei DM, Probabilistic Topic Models. Commun Acm. 2012;55(4):77–84. CCC:000302915000026.
73. La Rosa M, Fiannaca A, Rizzo R, Urso A, Probabilistic topic modeling for the analysis and classification of genomic sequences. BMC bioinformatics. 2015;16Suppl 6:S2. doi: 10.1186/1471-2105-16-S6-S225916734; PubMed Central PMCID: PMC4416183.
74. Konietzny SG, Dietz L, McHardy AC, Inferring functional modules of protein families with probabilistic topic models. BMC bioinformatics. 2011;12:141. doi: 10.1186/1471-2105-12-14121554720; PubMed Central PMCID: PMC3098182.
75. Falush D, Stephens M, Pritchard JK, Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003;164(4):1567–87. 12930761; PubMed Central PMCID: PMC1462648.
76. Pritchard JK, Stephens M, Donnelly P, Inference of population structure using multilocus genotype data. Genetics. 2000;155(2):945–59. 10835412; PubMed Central PMCID: PMC1461096.
77. Liu B, Liu L, Tsykin A, Goodall GJ, Green JE, Zhu M, et al. Identifying functional miRNA-mRNA regulatory modules with correspondence latent dirichlet allocation. Bioinformatics. 2010;26(24):3105–11. doi: 10.1093/bioinformatics/btq57620956247; PubMed Central PMCID: PMC2995118.
78. Verhaak RG, Hoadley KA, Purdom E, Wang V, Qi Y, Wilkerson MD, et al. Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1. Cancer Cell. 2010;17(1):98–110. doi: 10.1016/j.ccr.2009.12.02020129251; PubMed Central PMCID: PMC2818769.
79. Cancer Genome Atlas Research NetworkComprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061–8. doi: 10.1038/nature0738518772890; PubMed Central PMCID: PMC2671642.
80. Shen R, Mo Q, Schultz N, Seshan VE, Olshen AB, Huse J, et al. Integrative subtype discovery in glioblastoma using iCluster. PLoS ONE. 2012;7(4):e35236. doi: 10.1371/journal.pone.003523622539962; PubMed Central PMCID: PMC3335101.
81. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, Barabasi AL, The human disease network. Proc Natl Acad Sci U S A. 2007;104(21):8685–90. doi: 10.1073/pnas.070136110417502601; PubMed Central PMCID: PMC1885563.
82. van Driel MA, Bruggeman J, Vriend G, Brunner HG, Leunissen JA, A text-mining analysis of the human phenome. Eur J Hum Genet. 2006;14(5):535–42. doi: 10.1038/sj.ejhg.520158516493445.
83. Przytycka TM, Singh M, Slonim DK, Toward the dynamic interactome: it's about time. Brief Bioinform. 2010;11(1):15–29. doi: 10.1093/bib/bbp05720061351; PubMed Central PMCID: PMC2810115.
84. Chu LH, Lee E, Bader JS, Popel AS, Angiogenesis interactome and time course microarray data reveal the distinct activation patterns in endothelial cells. PLoS ONE. 2014;9(10):e110871. doi: 10.1371/journal.pone.011087125329517; PubMed Central PMCID: PMC4199761.
85. Faisal FE, Milenkovic T, Dynamic networks reveal key players in aging. Bioinformatics. 2014;30(12):1721–9. Epub 2014/02/21. doi: 10.1093/bioinformatics/btu08924554629.
86. Greene CS, Krishnan A, Wong AK, Ricciotti E, Zelaya RA, Himmelstein DS, et al. Understanding multicellular function and disease with human tissue-specific networks. Nature genetics. 2015;47(6):569–76. Epub 2015/04/29. doi: 10.1038/ng.325925915600.