[1] | Veenstra-Vanderweele J, Christian SL, Cook EH Jr (2004) Autism as a paradigmatic complex genetic disorder. Annu Rev Genomics Hum Genet 5: 379–405. doi: 10.1146/annurev.genom.5.061903.180050
|
[2] | Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, et al. (2010) Functional impact of global rare copy number variation in autism spectrum disorders. Nature 466: 368–372.
|
[3] | Schadt EE (2009) Molecular networks as sensors and drivers of common human diseases. Nature 461: 218–223. doi: 10.1038/nature08454
|
[4] | Gursoy A, Keskin O, Nussinov R (2008) Topological properties of protein interaction networks from a structural perspective. Biochem Soc Trans 36: 1398–1403. doi: 10.1042/bst0361398
|
[5] | Albert R (2005) Scale-free networks in cell biology. J Cell Sci 118: 4947–4957. doi: 10.1242/jcs.02714
|
[6] | Jeong H, Mason SP, Barabasi AL, Oltvai ZN (2001) Lethality and centrality in protein networks. Nature 411: 41–42. doi: 10.1038/35075138
|
[7] | Zotenko E, Mestre J, O'Leary DP, Przytycka TM (2008) 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 4: e1000140 doi:10.1371/journal.pcbi.1000140.
|
[8] | Jonsson PF, Bates PA (2006) Global topological features of cancer proteins in the human interactome. Bioinformatics 22: 2291–2297. doi: 10.1093/bioinformatics/btl390
|
[9] | Wachi S, Yoneda K, Wu R (2005) Interactome-transcriptome analysis reveals the high centrality of genes differentially expressed in lung cancer tissues. Bioinformatics 21: 4205–4208. doi: 10.1093/bioinformatics/bti688
|
[10] | De Las Rivas J, Fontanillo C (2010) Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLoS Comput Biol 6: e1000807 doi:10.1371/journal.pcbi.1000807.
|
[11] | Berggard T, Linse S, James P (2007) Methods for the detection and analysis of protein-protein interactions. Proteomics 7: 2833–2842. doi: 10.1002/pmic.200700131
|
[12] | Yu H, Braun P, Yildirim MA, Lemmens I, Venkatesan K, et al. (2008) High-quality binary protein interaction map of the yeast interactome network. Science 322: 104–110. doi: 10.1126/science.1158684
|
[13] | Shoemaker BA, Panchenko AR (2007) Deciphering protein-protein interactions. Part II. Computational methods to predict protein and domain interaction partners. PLoS Comput Biol 3: e43 doi:10.1371/journal.pcbi.0030043.
|
[14] | Levy ED, Landry CR, Michnick SW (2009) How perfect can protein interactomes be? Sci Signal 2: pe11. doi: 10.1126/scisignal.260pe11
|
[15] | Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 95: 14863–14868. doi: 10.1073/pnas.95.25.14863
|
[16] | The Gene Ontology in 2010: extensions and refinements. Nucleic Acids Res 38: D331–335. doi: 10.1093/nar/gkp1018
|
[17] | Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25: 25–29.
|
[18] | 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
|
[19] | Costello JC, Dalkilic MM, Beason SM, Gehlhausen JR, Patwardhan R, et al. (2009) Gene networks in Drosophila melanogaster: integrating experimental data to predict gene function. Genome Biol 10: R97. doi: 10.1186/gb-2009-10-9-r97
|
[20] | Guan Y, Myers CL, Lu R, Lemischka IR, Bult CJ, et al. (2008) A genomewide functional network for the laboratory mouse. PLoS Comput Biol 4: e1000165 doi:10.1371/journal.pcbi.1000165.
|
[21] | Ramani AK, Li Z, Hart GT, Carlson MW, Boutz DR, et al. (2008) A map of human protein interactions derived from co-expression of human mRNAs and their orthologs. Mol Syst Biol 4: 180. doi: 10.1038/msb.2008.19
|
[22] | Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, et al. (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7 Suppl 1: S7. doi: 10.1186/1471-2105-7-s1-s7
|
[23] | Peng J, Wang P, Zhou N, Zhu J (2009) Partial Correlation Estimation by Joint Sparse Regression Models. J Am Stat Assoc 104: 735–746. doi: 10.1198/jasa.2009.0126
|
[24] | Pe'er D (2005) Bayesian network analysis of signaling networks: a primer. Sci STKE 2005: l4. doi: 10.1126/stke.2812005pl4
|
[25] | Alterovitz G, Liu J, Afkhami E, Ramoni MF (2007) Bayesian methods for proteomics. Proteomics 7: 2843–2855. doi: 10.1002/pmic.200700422
|
[26] | Xuan NV, Chetty M, Coppel R, Wangikar PP (2012) Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network. BMC Bioinformatics 13: 131. doi: 10.1186/1471-2105-13-131
|
[27] | Zou M, Conzen SD (2005) A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21: 71–79. doi: 10.1093/bioinformatics/bth463
|
[28] | Hartwell LH, Hopfield JJ, Leibler S, Murray AW (1999) From molecular to modular cell biology. Nature 402: C47–52. doi: 10.1038/35011540
|
[29] | Adamcsek B, Palla G, Farkas IJ, Derényi I, Vicsek T (2006) CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22: 1021–1023. doi: 10.1093/bioinformatics/btl039
|
[30] | Altaf-Ul-Amin M, Shinbo Y, Mihara K, Kurokawa K, Kanaya S (2006) Development and implementation of an algorithm for detection of protein complexes in large interaction networks. BMC Bioinformatics 7: 207. doi: 10.1186/1471-2105-7-207
|
[31] | Arnau V, Mars S, Marín I (2005) Iterative Cluster Analysis of Protein Interaction Data. Bioinformatics 21: 364–378. doi: 10.1093/bioinformatics/bti021
|
[32] | Asthana S, King OD, Gibbons FD, Roth FP (2004) Predicting protein complex membership using probabilistic network reliability. Genome Res 14: 1170–1175. doi: 10.1101/gr.2203804
|
[33] | Bader GD, Hogue CW (2003) An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 4: 2.
|
[34] | Bader JS (2003) Greedily building protein networks with confidence. Bioinformatics 19: 1869–1874. doi: 10.1093/bioinformatics/btg358
|
[35] | Brun C, Chevenet F, Martin D, Wojcik J, Guenoche A, et al. (2003) Functional classification of proteins for the prediction of cellular function from a protein-protein interaction network. Genome Biol 5: R6. doi: 10.1186/gb-2003-5-1-r6
|
[36] | Dunn R, Dudbridge F, Sanderson CM (2005) The use of edge-betweenness clustering to investigate biological function in protein interaction networks. BMC Bioinformatics 6: 39.
|
[37] | Jiang P, Singh M (2010) SPICi: a fast clustering algorithm for large biological networks. Bioinformatics 26: 1105–1111. doi: 10.1093/bioinformatics/btq078
|
[38] | King AD, Przulj N, Jurisica I (2004) Protein complex prediction via cost-based clustering. Bioinformatics 20: 3013–3020. doi: 10.1093/bioinformatics/bth351
|
[39] | Luo F, Yang Y, Chen CF, Chang R, Zhou J, et al. (2007) Modular organization of protein interaction networks. Bioinformatics 23: 207–214. doi: 10.1093/bioinformatics/btl562
|
[40] | Navlakha S, Schatz MC, Kingsford C (2009) Revealing biological modules via graph summarization. J Comput Biol 16: 253–264. doi: 10.1089/cmb.2008.11tt
|
[41] | Newman ME (2006) Modularity and community structure in networks. Proc Natl Acad Sci U S A 103: 8577–8582. doi: 10.1073/pnas.0601602103
|
[42] | Pereira-Leal JB, Enright AJ, Ouzounis CA (2004) Detection of functional modules from protein interaction networks. Proteins 54: 49–57. doi: 10.1002/prot.10505
|
[43] | Qi Y, Balem F, Faloutsos C, Klein-Seetharaman J, Bar-Joseph Z (2008) Protein complex identification by supervised graph local clustering. Bioinformatics 24: i250–258. doi: 10.1093/bioinformatics/btn164
|
[44] | Rives AW, Galitski T (2003) Modular organization of cellular networks. Proc Natl Acad Sci U S A 100: 1128–1133. doi: 10.1073/pnas.0237338100
|
[45] | Spirin V, Mirny LA (2003) Protein complexes and functional modules in molecular networks. Proc Natl Acad Sci U S A 100: 12123–12128. doi: 10.1073/pnas.2032324100
|
[46] | Wang C, Ding C, Yang Q, Holbrook SR (2007) Consistent dissection of the protein interaction network by combining global and local metrics. Genome Biol 8: R271. doi: 10.1186/gb-2007-8-12-r271
|
[47] | Chen J, Yuan B (2006) Detecting functional modules in the yeast protein-protein interaction network. Bioinformatics 22: 2283–2290. doi: 10.1093/bioinformatics/btl370
|
[48] | Feng J, Jiang R, Jiang T (2011) A max-flow based approach to the identification of protein complexes using protein interaction and microarray data. IEEE/ACM Trans Comput Biol Bioinform 8: 621–634. doi: 10.1109/tcbb.2010.78
|
[49] | Maraziotis IA, Dimitrakopoulou K, Bezerianos A (2007) Growing functional modules from a seed protein via integration of protein interaction and gene expression data. BMC Bioinformatics 8: 408. doi: 10.1186/1471-2105-8-408
|
[50] | Tipney H, Hunter L (2010) An introduction to effective use of enrichment analysis software. Hum Genomics 4: 202–206. doi: 10.1186/1479-7364-4-3-202
|
[51] | Kim Y, Przytycka T (2012) Bridging the gap between genotype and phenotype via network approaches. Frontiers in Genetics special issue on mapping complex disease traits with global gene expression. Front Genet 3: 227. doi: 10.3389/fgene.2012.00227
|
[52] | Ideker T, Ozier O, Schwikowski B, Siegel AF (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 Suppl 1: S233–240. doi: 10.1093/bioinformatics/18.suppl_1.s233
|
[53] | O'Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, et al. (2012) Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature 485: 246–250. doi: 10.1038/nature10989
|
[54] | Vandin F, Upfal E, Raphael BJ (2011) Algorithms for detecting significantly mutated pathways in cancer. J Comput Biol 18: 507–522. doi: 10.1089/cmb.2010.0265
|
[55] | Rossin EJ, Lage K, Raychaudhuri S, Xavier RJ, Tatar D, et al. Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet 7: e1001273. doi: 10.1371/journal.pgen.1001273
|
[56] | Gilman SR, Iossifov I, Levy D, Ronemus M, Wigler M, et al. Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70: 898–907. doi: 10.1016/j.neuron.2011.05.021
|
[57] | The Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474: 609–615. doi: 10.1038/nature11453
|
[58] | Gilman SR, Iossifov I, Levy D, Ronemus M, Wigler M, et al. (2011) Rare de novo variants associated with autism implicate a large functional network of genes involved in formation and function of synapses. Neuron 70: 898–907. doi: 10.1016/j.neuron.2011.05.021
|
[59] | Levy D, Ronemus M, Yamrom B, Lee YH, Leotta A, et al. (2011) Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron 70: 886–897. doi: 10.1016/j.neuron.2011.05.015
|
[60] | Rossin EJ, Lage K, Raychaudhuri S, Xavier RJ, Tatar D, et al. (2011) Proteins encoded in genomic regions associated with immune-mediated disease physically interact and suggest underlying biology. PLoS Genet 7: e1001273 doi:10.1371/journal.pgen.1001273.
|
[61] | Chuang HY, Lee E, Liu YT, Lee D, Ideker T (2007) Network-based classification of breast cancer metastasis. Mol Syst Biol 3: 140. doi: 10.1038/msb4100180
|
[62] | Muller FJ, Laurent LC, Kostka D, Ulitsky I, Williams R, et al. (2008) Regulatory networks define phenotypic classes of human stem cell lines. Nature 455: 401–405. doi: 10.1038/nature07213
|
[63] | Mani KM, Lefebvre C, Wang K, Lim WK, Basso K, et al. (2008) A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol Syst Biol 4: 169. doi: 10.1038/msb.2008.2
|
[64] | Wang K NI, Banerjee N, Margolin AA, Califano A. Genome-wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes; 2006; Venice. pp. 348–362.
|
[65] | Xue H, Xian B, Dong D, Xia K, Zhu S, et al. (2007) A modular network model of aging. Mol Syst Biol 3: 147. doi: 10.1038/msb4100189
|
[66] | Xia K, Xue H, Dong D, Zhu S, Wang J, et al. (2006) Identification of the proliferation/differentiation switch in the cellular network of multicellular organisms. PLoS Comput Biol 2: e145 doi:10.1371/journal.pcbi.0020145.
|
[67] | Ulitsky I, Krishnamurthy A, Karp RM, Shamir R (2010) DEGAS: de novo discovery of dysregulated pathways in human diseases. PLoS ONE 5: e13367 doi:10.1371/journal.pone.0013367.
|
[68] | Chowdhury SA, Koyuturk M (2010) Identification of coordinately dysregulated subnetworks in complex phenotypes. Pac Symp Biocomput 133–144. doi: 10.1142/9789814295291_0016
|
[69] | Kim YA, Wuchty S, Przytycka TM (2011) Identifying causal genes and dysregulated pathways in complex diseases. PLoS Comput Biol 7: e1001095 doi:10.1371/journal.pcbi.1001095.
|
[70] | Kim Y, Salari R, Wuchty S, Przytycka TM (2013) Module Cover – a new approach to genotype-phenotype studies;. Pacyfic Synposium on Biocomputing 18: 103–110. doi: 10.1142/9789814447973_0014
|
[71] | Vandin F, Upfal E, Raphael BJ (2012) De novo discovery of mutated driver pathways in cancer. Genome Res 22: 375–385. doi: 10.1101/gr.120477.111
|
[72] | Stranger BE, Forrest MS, Clark AG, Minichiello MJ, Deutsch S, et al. (2005) Genome-wide associations of gene expression variation in humans. PLoS Genet 1: e78 doi:10.1371/journal.pgen.0010078.
|
[73] | Stranger BE, Nica AC, Forrest MS, Dimas A, Bird CP, et al. (2007) Population genomics of human gene expression. Nat Genet 39: 1217–1224. doi: 10.1038/ng2142
|
[74] | Managbanag JR, Witten TM, Bonchev D, Fox LA, Tsuchiya M, et al. (2008) Shortest-path network analysis is a useful approach toward identifying genetic determinants of longevity. PLoS ONE 3: e3802 doi:10.1371/journal.pone.0003802.
|
[75] | Shih YK, Parthasarathy S (2012) A single source k-shortest paths algorithm to infer regulatory pathways in a gene network. Bioinformatics 28: i49–58. doi: 10.1093/bioinformatics/bts212
|
[76] | Carter GW, Prinz S, Neou C, Shelby JP, Marzolf B, et al. (2007) Prediction of phenotype and gene expression for combinations of mutations. Mol Syst Biol 3: 96. doi: 10.1038/msb4100137
|
[77] | Bailly-Bechet M, Borgs C, Braunstein A, Chayes J, Dagkessamanskaia A, et al. (2011) Finding undetected protein associations in cell signaling by belief propagation. Proc Natl Acad Sci U S A 108: 882–887. doi: 10.1073/pnas.1004751108
|
[78] | Tuncbag N, McCallum S, Huang SS, Fraenkel E (2012) SteinerNet: a web server for integrating ‘omic’ data to discover hidden components of response pathways. Nucleic Acids Res 40: W505–509. doi: 10.1093/nar/gks445
|
[79] | Tu Z, Wang L, Arbeitman MN, Chen T, Sun F (2006) An integrative approach for causal gene identification and gene regulatory pathway inference. Bioinformatics 22: e489–496. doi: 10.1093/bioinformatics/btl234
|
[80] | Suthram S, Beyer A, Karp RM, Eldar Y, Ideker T (2008) eQED: an efficient method for interpreting eQTL associations using protein networks. Mol Syst Biol 4: 162. doi: 10.1038/msb.2008.4
|
[81] | Yeger-Lotem E, Riva L, Su LJ, Gitler AD, Cashikar AG, et al. (2009) Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat Genet 41: 316–323. doi: 10.1038/ng.337
|
[82] | Lee E, Jung H, Radivojac P, Kim JW, Lee D (2009) Analysis of AML genes in dysregulated molecular networks. BMC Bioinformatics 10 Suppl 9: S2. doi: 10.1186/1471-2105-10-s9-s2
|
[83] | Kohler 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
|
[84] | Missiuro PV, Liu K, Zou L, Ross BC, Zhao G, et al. (2009) Information flow analysis of interactome networks. PLoS Comput Biol 5: e1000350 doi:10.1371/journal.pcbi.1000350.
|
[85] | Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M (2005) Whole-proteome prediction of protein function via graph-theoretic analysis of interaction maps. Bioinformatics 21 Suppl 1: i302–310. doi: 10.1093/bioinformatics/bti1054
|
[86] | Newman M (2005) A measure of betweenness centrality based on random walks. Social Networks 27: 39–54. doi: 10.1016/j.socnet.2004.11.009
|
[87] | Stojmirovic A, Yu YK (2007) Information flow in interaction networks. J Comput Biol 14: 1115–1143. doi: 10.1089/cmb.2007.0069
|
[88] | 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.
|
[89] | Doyle PGSJ (1984) Random walks and electric networks. doi: 10.5948/upo9781614440222.004
|
[90] | Kim YA, Przytycki JH, Wuchty S, Przytycka TM (2011) Modeling information flow in biological networks. Phys Biol 8: 035012. doi: 10.1088/1478-3975/8/3/035012
|
[91] | Chowdhury SA, Nibbe RK, Chance MR, Koyuturk M (2011) Subnetwork state functions define dysregulated subnetworks in cancer. J Comput Biol 18: 263–281. doi: 10.1089/cmb.2010.0269
|
[92] | Dao P, Colak R, Salari R, Moser F, Davicioni E, et al. (2010) Inferring cancer subnetwork markers using density-constrained biclustering. Bioinformatics 26: i625–631. doi: 10.1093/bioinformatics/btq393
|
[93] | Dao P, Wang K, Collins C, Ester M, Lapuk A, et al. (2011) Optimally discriminative subnetwork markers predict response to chemotherapy. Bioinformatics 27: i205–213. doi: 10.1093/bioinformatics/btr245
|
[94] | Lee E, Chuang HY, Kim JW, Ideker T, Lee D (2008) Inferring pathway activity toward precise disease classification. PLoS Comput Biol 4: e1000217 doi: 10.1371/journal.pcbi.1000217.
|
[95] | Kelley BP, Sharan R, Karp RM, Sittler T, Root DE, et al. (2003) Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci U S A 100: 11394–11399. doi: 10.1073/pnas.1534710100
|
[96] | Suthram S, Dudley JT, Chiang AP, Chen R, Hastie TJ, et al. (2010) Network-based elucidation of human disease similarities reveals common functional modules enriched for pluripotent drug targets. PLoS Comput Biol 6: e1000662 doi:10.1371/journal.pcbi.1000662.
|
[97] | Chu LH, Chen BS (2008) Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets. BMC Syst Biol 2: 56. doi: 10.1186/1752-0509-2-56
|
[98] | Nayak RR, Kearns M, Spielman RS, Cheung VG (2009) Coexpression network based on natural variation in human gene expression reveals gene interactions and functions. Genome Res 19: 1953–1962. doi: 10.1101/gr.097600.109
|