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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Mini-Review Article

Complex Networks, Gene Expression and Cancer Complexity: A Brief Review of Methodology and Applications

Author(s): A.C. Iliopoulos, G. Beis, P. Apostolou and I. Papasotiriou*

Volume 15, Issue 6, 2020

Page: [629 - 655] Pages: 27

DOI: 10.2174/1574893614666191017093504

Price: $65

Abstract

In this brief survey, various aspects of cancer complexity and how this complexity can be confronted using modern complex networks’ theory and gene expression datasets, are described. In particular, the causes and the basic features of cancer complexity, as well as the challenges it brought are underlined, while the importance of gene expression data in cancer research and in reverse engineering of gene co-expression networks is highlighted. In addition, an introduction to the corresponding theoretical and mathematical framework of graph theory and complex networks is provided. The basics of network reconstruction along with the limitations of gene network inference, the enrichment and survival analysis, evolution, robustness-resilience and cascades in complex networks, are described. Finally, an indicative and suggestive example of a cancer gene co-expression network inference and analysis is given.

Keywords: Cancer complexity, gene co-expression, complex networks, network inference, network evolution, colon cancer.

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[1]
Sudhakar A. History of cancer, ancient and modern treatment methods. J Cancer Sci Ther 2009; 1(2): 1-4.
[http://dx.doi.org/10.4172/1948-5956.100000e2]
[2]
World Health Organization. Available at: https://www.who.int/cancer/en/
[3]
Hanahan D, Weinberg RA. Hallmarks of Cancer: The next generation. Cell 2011; 144: 646-74.
[http://dx.doi.org/10.1016/j.cell.2011.02.013]
[4]
Butcher EC, Berg EL, Kunkel EJ. Systems biology in drug discovery. Nat Biotechnol 2004; 22(10): 1253-9.
[http://dx.doi.org/10.1038/nbt1017]
[5]
Hornberg JJ, Bruggemana FJ, Westerhoff HV, et al. Cancer: a systems biology disease. Biosystems 2006; 83: 81-90.
[http://dx.doi.org/10.1016/j.biosystems.2005.05.014]
[6]
Grizzi F, Chiriva-Internati M. Cancer: looking for simplicity and finding complexity. Cancer Cell Int 2006; 6: 4.
[http://dx.doi.org/10.1186/1475-2867-6-4]
[7]
Chen F, Zhuang X, Lin L, et al. New horizons in tumor microenvironment biology: challenges and opportunities. BMC Med 2015; 13: 45.
[http://dx.doi.org/10.1186/s12916-015-0278-7]
[8]
Orimo A, Gupta PB, Sgroi DC, et al. Stromal fibroblasts present in invasive human breast carcinomas promote tumor growth and angiogenesis through elevated SDF-1/CXCL12 secretion. Cell 2005; 121: 335-48.
[http://dx.doi.org/10.1016/j.cell.2005.02.034]
[9]
Wang M, Zhao J, Zhang L, et al. Role of tumor microenvironment in tumorigenesis. J Cancer 2017; 8(5): 761-73.
[http://dx.doi.org/10.7150/jca.17648]
[10]
Moore NM, Kuhn NZ, Hanlon SE, Lee JSH, Nagahara LA. De-convoluting cancer’s complexity: using a ‘physical sciences lens’ to provide a different (clearer) perspective of cancer. Phys Biol 2011; 8010302
[http://dx.doi.org/10.1088/1478-3975/8/1/010302]
[11]
Koutsogiannouli E, Papavassiliou AG, Papanikolaou NA. Complexity in cancer biology:Is systems biology the answer? Cancer Med 2013; 2(2): 164-77.
[http://dx.doi.org/10.1002/cam4.62]
[12]
Spillman WB Jr, Robertson JL, Huckle WR, Govindan BS, Meissner KE. Complexity, fractals, disease time, and cancer. Phys Rev E Stat Nonlin Soft Matter Phys 2004; 70061911
[http://dx.doi.org/10.1103/PhysRevE.70.061911]
[13]
Dokukin ME, Guz NV, Gaikwad RM, Woodworth CD, Sokolov I. Cell surface as a fractal: normal and cancerous cervical cells demonstrate different fractal behavior of surface adhesion maps at the nanoscale. Phys Rev Lett 2011; 107(2)028101
[http://dx.doi.org/10.1103/PhysRevLett.107.028101]
[14]
Crawford SAA. “Chaotic” Approach to the Treatment of Advanced Cancer. J Tradit Med Clin Natur 2017; 6: 3.
[http://dx.doi.org/10.4172/2573-4555.1000232]
[15]
Lennon FE, Cianci GC, Cipriani NA, et al. Lung cancer-a fractal viewpoint. Nat Rev Clin Oncol 2015; 12(11): 664-75.
[http://dx.doi.org/10.1038/nrclinonc.2015.108]
[16]
Lopes R, Betrouni N. Fractal and multifractal analysis: A review. Med Image Anal 2009; 13: 634-49.
[http://dx.doi.org/10.1016/j.media.2009.05.003]
[17]
Blackadar CB. Historical review of the causes of cancer. World J Clin Oncol 2016; 7(1): 54-86.
[http://dx.doi.org/10.5306/wjco.v7.i1.54]
[18]
Williams SCP. News feature: capturing cancer’s complexity. Proc Natl Acad Sci USA 2015; 112(15): 4509-11.
[http://dx.doi.org/10.1073/pnas.1500963112]
[19]
Aderem A. Systems biology: its practice and challenges. Cell 2005; 121: 511-3.
[http://dx.doi.org/10.1016/j.cell.2005.04.020]
[20]
Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000; 100: 57-70.
[http://dx.doi.org/10.1016/S0092-8674(00)81683-9]
[21]
Kitano H. Biological robustness. Nat Rev Genet 2004; 5: 826-37.
[http://dx.doi.org/10.1038/nrg1471]
[22]
Kitano H. Cancer as a robust system: Implications for anticancer therapy. Nat Rev Cancer 2004; 4: 227-35.
[http://dx.doi.org/10.1038/nrc1300]
[23]
Gentles AJ, Gallahan D. Systems biology: confronting the complexity of cancer. Cancer Res 2011; 71(18): 5961-4.
[http://dx.doi.org/10.1158/0008-5472.CAN-11-1569]
[24]
Biemar F, Foti M. Global progress against cancer-challenges and opportunities. Cancer Biol Med 2013; 10: 183-6.
[25]
Meyskens FL Jr, Mukhtar H, Rock CL, et al. Cancer prevention: obstacles, challenges, and the road ahead JNCI. J Natl Cancer Inst 2016; 108(2)djv309
[26]
Cagan R, Meyer P. Rethinking cancer: current challenges and opportunities in cancer research. Dis Model Mech 2017; 10: 349-52.
[http://dx.doi.org/10.1242/dmm.030007]
[27]
Kreeger PK, Lauffenburger DA. Cancer systems biology: a network modeling perspective. Carcinogenesis 2010; 31(1): 2-8.
[http://dx.doi.org/10.1093/carcin/bgp261]
[28]
Wang WC, Zhang XF, Peng J, et al. Survival mechanisms and influence factors of circulating tumor cells. BioMed Res Int 2018.Article ID 6304701
[http://dx.doi.org/10.1155/2018/6304701]
[29]
Narrandes S, Xu W. Gene expression detection assay for cancer clinical use. J Cancer 2018; 9(13): 2249-65.
[http://dx.doi.org/10.7150/jca.24744]
[30]
Slonim DK, Yanai I. Getting started in gene expression microarray analysis. PLOS Comput Biol 2009; 5(10)e1000543
[http://dx.doi.org/10.1371/journal.pcbi.1000543]
[31]
Zhang L, Zhou W, Velculescu VE, et al. Gene expression profiles in normal and cancer cells. Science 1997; 276: 1268-72.
[http://dx.doi.org/10.1126/science.276.5316.1268]
[32]
Ramaswamy S, Tamayo P, Rifkin R. Multiclass cancer diagnosis using tumor gene expression signatures. Proc Natl Acad Sci USA 2001; 98(26): 15149-54.
[http://dx.doi.org/10.1073/pnas.211566398]
[33]
Macgregor PF, Squire JA. Application of microarrays to the analysis of gene expression in cancer. Clin Chem 2002; 48(8): 1170-7.
[http://dx.doi.org/10.1093/clinchem/48.8.1170]
[34]
Ross DT, Scherf U, Eisen MB, et al. Systematic variation in gene expression patterns in human cancer cell lines. Nat Genet 2000; 24: 227-35.
[http://dx.doi.org/10.1038/73432]
[35]
Golub TR, Slonim TR, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531-7.
[http://dx.doi.org/10.1126/science.286.5439.531]
[36]
Su AI, Welsh JB, Sapinoso LM, et al. Molecular classification of human carcinomas by use of gene expression signatures. Cancer Res 2001; 61: 7388-93.
[37]
Dudoit S, Fridlyand J, Speed TP. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 2002; 97(457): 77-87.
[http://dx.doi.org/10.1198/016214502753479248]
[38]
Salem H, Attiya G, El-Fishawy N. Classification of human cancer diseases by gene expression profiles. Appl Soft Comput 2017; 50: 124-34.
[http://dx.doi.org/10.1016/j.asoc.2016.11.026]
[39]
Van’t Veer LJ, Dai H, Van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002; 415: 530-5.
[http://dx.doi.org/10.1038/415530a]
[40]
Riester M, Wu H-J, Zehir A, et al. Distance in cancer gene expression from stem cells predicts patient survival. PLoS One 2017; 12(3)e0173589
[http://dx.doi.org/10.1371/journal.pone.0173589]
[41]
Apostolou P, Toloudi M, Chatziioannou M, Ioannou E, Papasotiriou I. Cancer stem cells stemness transcription factors expression correlates with breast cancer disease stage. Curr Stem Cell Res Ther 2012; 7(6): 415-9.
[http://dx.doi.org/10.2174/157488812804484639]
[42]
Bild AH, Yao G, Chang JT, et al. Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 2006; (439): 353-7.
[http://dx.doi.org/10.1038/nature04296]
[43]
Kamel HFM, Bagader Al-Amodi HAS. Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine. Genomics Proteomics Bioinformatics 2017; 15: 220-35.
[http://dx.doi.org/10.1016/j.gpb.2016.11.005]
[44]
Apostolou P, Toloudi M, Papasotiriou I. Identification of genes involved in breast cancer and breast cancer stem cells. Breast Cancer (Dove Med Press) 2015; 7: 183-91.
[http://dx.doi.org/10.2147/BCTT.S85202]
[45]
Folgueira MA, Carraro DM, Brentani H, et al. Gene expression profile associated with response to doxorubicin-based therapy in breast cancer. Clin Cancer Res 2005; 11(20): 7434-43.
[http://dx.doi.org/10.1158/1078-0432.CCR-04-0548]
[46]
Tang Z, Zeng Q, Li Y, et al. Predicting radiotherapy response for patients with soft tissue sarcoma by developing a molecular signature. Oncol Rep 2017; 38(5): 2814-24.
[http://dx.doi.org/10.3892/or.2017.5999]
[47]
Boccaletti S, Latora V, Morenod Y, et al. Complex networks: structure and dynamics. Phys Rep 2006; 424(4-5): 175-308.
[http://dx.doi.org/10.1016/j.physrep.2005.10.009]
[48]
Cheng TMK, Gulati S, Agius R, et al. Understanding cancer mechanisms through network dynamics. Brief Funct Genomics 2012; 2(6): 543-60.
[http://dx.doi.org/10.1093/bfgp/els025]
[49]
Chorozoglou D, Iliopoulos A, Kourouklas C, et al. Earthquake networks as a tool for seismicity investigation: a review. Pure Appl Geophys 2019; 176: 4649-60.
[http://dx.doi.org/10.1007/s00024-019-02253-w]
[50]
Da Costa FL, Rodrigues FA, Travieso G, et al. Characterization of Complex Networks: A Survey of measurements. Adv Phys 2007; 56(1): 167-242.
[http://dx.doi.org/10.1080/00018730601170527]
[51]
Newman MEJ. The structure and Function of Complex Networks. SIAM Rev 2003; 45(2): 167-256.
[http://dx.doi.org/10.1137/S003614450342480]
[52]
Iliopoulos AC. Complex systems: phenomenology, modeling, analysis. Int J Appl Exp Math 2016; 1: 11.
[53]
Rubinov M, Sporns O. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 2010; 52: 1059-69.
[http://dx.doi.org/10.1016/j.neuroimage.2009.10.003]
[54]
Pavlopoulos GA, Secrier M, Moschopoulos CN, et al. Using graph theory to analyze biological networks. BioData Min 2011; 4(10): 1-27.
[http://dx.doi.org/10.1186/1756-0381-4-10]
[55]
Silva CT, Zhao L. Machine Learning in Complex Networks. Springer 2016; p. 331.
[http://dx.doi.org/10.1007/978-3-319-17290-3]
[56]
Holme P, Saramäki J. Temporal networks. Phys Rep 2012; 519: 97-125.
[http://dx.doi.org/10.1016/j.physrep.2012.03.001]
[57]
Kivelä M, Arenas A, Barthelemy MJ, et al. Multilayer networks. J Complex Netw 2014; 2(3): 203-71.
[http://dx.doi.org/10.1093/comnet/cnu016]
[58]
Boccaletti S, Bianconi G, Criado R, et al. The structure and dynamics of multilayer networks. Phys Rep 2014; 544: 1-122.
[http://dx.doi.org/10.1016/j.physrep.2014.07.001]
[59]
Da Costa FL, Oliveira ON Jr, Travieso G, et al. Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Adv Phys 2011; 60(3): 329-412.
[http://dx.doi.org/10.1080/00018732.2011.572452]
[60]
Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005; 4(1): Article 17.
[http://dx.doi.org/10.2202/1544-6115.1128]
[61]
Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008; 9: 559.
[http://dx.doi.org/10.1186/1471-2105-9-559]
[62]
Angelin-Bonnet O, Biggs PJ, Vignes M. Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling.In: Sanguinetti G, Huynh-Thu V (eds) Gene Regulatory Networks Methods Mol Biol 2019; 1883 Humana Press, New York.
[http://dx.doi.org/10.1007/978-1-4939-8882-2_15]
[63]
Delgado FM, Gómez-Vela F. Computational methods for gene regulatory networks reconstruction and analysis: a review. Artif Intell Med 2019; 95: 133-45.
[http://dx.doi.org/10.1016/j.artmed.2018.10.006]
[64]
Segal E, Friedman N, Kaminski N, et al. From signatures to models: understanding cancer using microarrays. Nat Genet 2005; 37: S38-45.
[http://dx.doi.org/10.1038/ng1561]
[65]
Unnithan SKR, Kannan B, Jathavedan M. Betweenness centrality in some classes of graphs. J Inter Combinator 2014. Article ID: 241723
[66]
Koschützki D, Schreiber F. Centrality analysis methods for biological networks and their application to gene regulatory networks. Gene Regul Syst Bio 2008; 2: 193-201.
[http://dx.doi.org/10.4137/GRSB.S702]
[67]
Estrada E, Rodríguez-Velázquez JA. Subgraph centrality in complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 2005; 71056103
[http://dx.doi.org/10.1103/PhysRevE.71.056103]
[68]
Hwang W, Cho Y, Zhang A, et al. Bridging Centrality: Identifying Bridging Nodes In Scale-free Networks KDD Philadelphia, USA 2006.
[69]
Korn A, Schubert A, Telcs A. Lobby index in networks. Physica A 2009; 388: 2221-6.
[http://dx.doi.org/10.1016/j.physa.2009.02.013]
[70]
Campiteli MG, Holandab AJ, Soares LDH, et al. Lobby index as a network centrality measure. Physica A 2013; 392: 5511-5.
[http://dx.doi.org/10.1016/j.physa.2013.06.065]
[71]
Shanahan M, Wildie M. Knotty-Centrality: finding the connective core of a complex network. PLoS One 2012; 7(5)e36579
[http://dx.doi.org/10.1371/journal.pone.0036579]
[72]
Humphries MD, Gurney K. Network small-world-ness: a quantitative method for determining canonical network equivalence. PLoS One 2008; 3(4)e0002051
[http://dx.doi.org/10.1371/journal.pone.0002051]
[73]
Watts DJ, Strogatz SH. Collective dynamics of small world networks. Nature 1998; 393: 440-2.
[http://dx.doi.org/10.1038/30918]
[74]
Newman MEJ. Scientific collaboration networks. I. Network construction and fundamental results. Phys Rev E Stat Nonlin Soft Matter Phys 2001; 64016131
[http://dx.doi.org/10.1103/PhysRevE.64.016131]
[75]
Soffer SN, Vázquez A. Network clustering coefficient without degree-correlation biases. Phys Rev E Stat Nonlin Soft Matter Phys 2005; 71057101
[http://dx.doi.org/10.1103/PhysRevE.71.057101]
[76]
Jiang B, Claramunt C. Topological analysis of urban street networks. Environ Plann B Plann Des 2004; 31: 51-162.
[http://dx.doi.org/10.1068/b306]
[77]
Yin H, Benson AR. Higher-order clustering in networks. Phys Rev E 2018; 97052306
[http://dx.doi.org/10.1103/PhysRevE.97.052306]
[78]
Guimera R, Amaral L. Functional cartography of complex metabolic networks. Nature 2005; 433: 895-900.
[http://dx.doi.org/10.1038/nature03288]
[79]
Colizza V, Flammini A, Serrano MA, et al. Detecting rich-club ordering in complex networks. Nat Phys 2006; 2: 110-5.
[http://dx.doi.org/10.1038/nphys209]
[80]
Ramadan E, Alinsaif S. Network topology measures for identifying disease-gene association in breast cancer. BMC Bioinformatics 2016; 17(7): 274.
[http://dx.doi.org/10.1186/s12859-016-1095-5]
[81]
Segal E, Friedman N, Koller D, et al. A module map showing conditional activity of expression modules in cancer. Nat Genet 2004; 36(10): 1090-8.
[http://dx.doi.org/10.1038/ng1434]
[82]
Danon L, Dìaz-Guilera A, Duch J, et al. Comparing community structure identification. J Stat Mech 2005.P09008
[83]
Newman MEJ. Detecting community structure in networks. Eur Phys J B 2004; 38: 321-30.
[http://dx.doi.org/10.1140/epjb/e2004-00124-y]
[84]
Newman MEJ, Girvan M. Finding and evaluating community structure in networks. Phys Rev E Stat Nonlin Soft Matter Phys 2004; 69026113
[http://dx.doi.org/10.1103/PhysRevE.69.026113]
[85]
Van Dongen S. Graph Clustering by Flow Simulation PhD Thesis, University of Utrecht (Netherlands) 2000.
[86]
Enright AJ, Van Dongen S, Ouzounis CA. An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res 2002; 30: 1575-8.
[http://dx.doi.org/10.1093/nar/30.7.1575]
[87]
Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics 2003; 4: 2.
[http://dx.doi.org/10.1186/1471-2105-4-2]
[88]
King AD, Pržulj N, Jurisica I. Protein complex prediction via cost-based clustering. Bioinformatics 2004; 20(17): 3013-20.
[http://dx.doi.org/10.1093/bioinformatics/bth351]
[89]
Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci USA 2006; 103(23): 8577-82.
[http://dx.doi.org/10.1073/pnas.0601602103]
[90]
Liu G, Wong L, Chua HN. Complex discovery from weighted PPI networks. Bioinformatics 2009; 25(15): 1891-7.
[http://dx.doi.org/10.1093/bioinformatics/btp311]
[91]
Adamcsek B, Palla G, Farkas IJ, et al. CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 2006; 22(8): 1021-3.
[http://dx.doi.org/10.1093/bioinformatics/btl039]
[92]
Gregory S. Finding overlapping communities in networks by label propagation. New J Phys 2010; 12103018
[http://dx.doi.org/10.1088/1367-2630/12/10/103018]
[93]
Jiang P, Singh M. SPICi: a fast clustering algorithm for large biological networks. Bioinformatics 2010; 26(8): 1105-11.
[http://dx.doi.org/10.1093/bioinformatics/btq078]
[94]
Rhrissorrakrai K, Gunsalus KC. MINE: Module Identification in Networks. BMC Bioinformatics 2011; 12: 192.
[http://dx.doi.org/10.1186/1471-2105-12-192]
[95]
Nepusz T, Yu H, Paccanaro A. Detecting overlapping protein complexes in protein-protein interaction networks. Nat Methods 2012; 9(5): 471-2.
[http://dx.doi.org/10.1038/nmeth.1938]
[96]
Fortunato S. Community detection in graphs. Phys Rep 2010; 486: 75-174.
[http://dx.doi.org/10.1016/j.physrep.2009.11.002]
[97]
Bhowmick SS, Seah BS. Clustering and summarizing protein-protein interaction networks: a survey. IEEE Trans Knowl Data Eng 2016; 28(3): 638-58.
[http://dx.doi.org/10.1109/TKDE.2015.2492559]
[98]
Ding Z, Zhang X, Sun D, et al. Overlapping community detection based on network decomposition. Sci Rep 2016; 6: 24115.
[http://dx.doi.org/10.1038/srep24115]
[99]
Kouhsar M, Zare-Mirakabad F, Jamali Y. WCOACH: protein complex prediction in weighted PPI networks. Genes Genet Syst 2015; 90: 317-24.
[http://dx.doi.org/10.1266/ggs.15-00032]
[100]
Vella D, Marini S, Vitali F, et al. MTGO: PPI network analysis via topological and functional module identification. Sci Rep 2018; 8: 5499.
[http://dx.doi.org/10.1038/s41598-018-23672-0]
[101]
Milo R, Shen-Orr S, Itzkovitz S, et al. Network motifs: simple building blocks of complex networks. Science 2002; 298: 824.
[http://dx.doi.org/10.1126/science.298.5594.824]
[102]
Kashtan N, Itzkovitz S, Milo R, et al. Topological generalizations of network motifs. Phys Rev E Stat Nonlin Soft Matter Phys 2004; 70031909
[http://dx.doi.org/10.1103/PhysRevE.70.031909]
[103]
Wong E, Baur B, Quader S, et al. Biological network motif detection: Principles and practice. Brief Bioinform 2012; 13(2): 202-15.
[http://dx.doi.org/10.1093/bib/bbr033]
[104]
Wernicke S, Rasche F. FANMOD: a tool for fast network motif detection. Bioinformatics 2006; 22(9): 1152-3.
[http://dx.doi.org/10.1093/bioinformatics/btl038]
[105]
Kashani ZRM, Ahrabian H, Elahi E, et al. Kavosh: a new algorithm for finding network motifs. BMC Bioinformatics 2009; 10: 318.
[http://dx.doi.org/10.1186/1471-2105-10-318]
[106]
Omidi S, Schreiber F, Masoudi-Nejad A. MODA: an efficient algorithm for network motif discovery in biological networks. Genes Genet Syst 2009; 84: 385-95.
[http://dx.doi.org/10.1266/ggs.84.385]
[107]
Strogatz SH. Exploring complex networks. Nature 2001; 410: 268-76.
[http://dx.doi.org/10.1038/35065725]
[108]
Wang XF, Chen G. Complex networks: small-world, scale-free and beyond circuits and systems magazine. IEEE Circuits Syst Mag 2003; 3: 6-20.
[109]
Erdös P, Rényi A. On random graphs. Publicationes Mathematicae 1959; 6: 290-7.
[110]
Bender EA, Canfield ER. The asymptotic number of labelled graphs with given degree sequences. J Comb Theory Ser A 1978; 24: 296-307.
[http://dx.doi.org/10.1016/0097-3165(78)90059-6]
[111]
Song C, Havlin S, Makse HA. Self-similarity of complex networks. Nature 2005; 433: 392-5.
[http://dx.doi.org/10.1038/nature03248]
[112]
Barabási A-L, Albert R. Emergence of scaling in random networks. Science 1999; 286: 509-12.
[http://dx.doi.org/10.1126/science.286.5439.509]
[113]
R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna, Austria 2008.
[114]
Csardi G, Nepusz T. The igraph software package for complex network research. Inter J Complex Syst 2006.
[115]
D’haeseleer P, Liang S, Somogyi R. Genetic network inference: from co-expression clustering to reverse engineering. Bioinformatics 2000; 16(8): 707-26.
[http://dx.doi.org/10.1093/bioinformatics/16.8.707]
[116]
Lopes FM, Cesar RM, Da F, Costa L. Gene expression complex networks: synthesis, identification, and analysis. J Comput Biol 2011; 18(10): 1353-67.
[http://dx.doi.org/10.1089/cmb.2010.0118]
[117]
De Jong H. Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol 2002; 9(1): 67-103.
[http://dx.doi.org/10.1089/10665270252833208]
[118]
Brazhnik P, De la Fuente A, Mendes P. Gene networks: how to put the function in genomics. Trends Biotechnol 2002; 11(20): 467-72.
[http://dx.doi.org/10.1016/S0167-7799(02)02053-X]
[119]
Tegnér J, Yeung MKS, Hasty J, et al. Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA 2003; 100(10): 5944-9.
[http://dx.doi.org/10.1073/pnas.0933416100]
[120]
Yu J, Smith VA, Wang PP, et al. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 2004; 20(18): 3594-603.
[http://dx.doi.org/10.1093/bioinformatics/bth448]
[121]
Bansal M, Belcastro V, Impiombato AA, et al. How to infer gene networks from expression profiles. Mol Syst Biol 2007; 3: 78.
[http://dx.doi.org/10.1038/msb4100120]
[122]
Zampieri M, Soranzo N, Altafini C. Discerning static and causal interactions in genome-wide reverse engineering problems. Bioinformatics 2008; 24(13): 1510-5.
[http://dx.doi.org/10.1093/bioinformatics/btn220]
[123]
Xulvi-Brunet R, Li H. Co-expression networks: graph properties and topological comparisons. Bioinformatics 2010; 26(2): 205-14.
[http://dx.doi.org/10.1093/bioinformatics/btp632]
[124]
Stifanelli PF, Creanza TM, Anglani R, et al. A comparative study of covariance selection models for the inference of gene regulatory networks. J Biomed Inform 2013; 46: 894-904.
[http://dx.doi.org/10.1016/j.jbi.2013.07.002]
[125]
Khosravi P, Gazestani VH, Pirhaji L, et al. Inferring interaction type in gene regulatory networks using co-expression data. Algorithms Mol Biol 2015; 10: 23.
[http://dx.doi.org/10.1186/s13015-015-0054-4]
[126]
Li J, Li Y-X, Li Y-Y. Differential regulatory analysis based on co-expression network in cancer research. Biomed Res Int. Vol 2016.
[127]
Van Dam S, Võsa U, Van der Graaf A, et al. Gene co-expression analysis for functional classification and gene-disease predictions. Brief Bioinform 2018; 19(4): 575-92.
[128]
Carter SL, Brechbühler CM, Griffin M, et al. Gene co-expression network topology provides a framework for molecular character-rization of cellular state. Bioinformatics 2004; 20(14): 2242-50.
[http://dx.doi.org/10.1093/bioinformatics/bth234]
[129]
Statnikov A, Aliferis CF, Tsamardinos I, et al. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 2005; 21(5): 631-43.
[http://dx.doi.org/10.1093/bioinformatics/bti033]
[130]
Aoki K, Ogata Y, Shibata D. Approaches for extracting practical information from gene co-expression networks in plant biology. Plant Cell Physiol 2007; 48(3): 381-90.
[http://dx.doi.org/10.1093/pcp/pcm013]
[131]
Ruan J, Dean AK, Zhang W. A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC Syst Biol 2010; 4: 8.
[http://dx.doi.org/10.1186/1752-0509-4-8]
[132]
Kumari S, Nie J, Chen H-S, et al. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery. PLoS One 2012; 7(11)e50411
[http://dx.doi.org/10.1371/journal.pone.0050411]
[133]
Song L, Langfelder P, Horvath S. Comparison of co-expression measures: mutual information, correlation, and model based indices. BMC Bioinformatics 2012; 13: 328.
[http://dx.doi.org/10.1186/1471-2105-13-328]
[134]
De S, Santos S, Takahashi DY, Nakata A, et al. A comparative study of statistical methods used to identify dependencies between gene expression signals. Brief Bioinform 2014; 15(6): 906-18.
[http://dx.doi.org/10.1093/bib/bbt051]
[135]
Zheng C-H, Yuan L, Sha W, et al. Gene differential co-expression analysis based on bi-weight correlation and maximum clique. BMC Bioinformatics 2014; 15(Suppl. 15): S3.
[http://dx.doi.org/10.1186/1471-2105-15-S15-S3]
[136]
Kraskov A, Stögbauer H, Grassberger P. Estimating mutual information. Phys Rev E Stat Nonlin Soft Matter Phys 2004; 69066138
[http://dx.doi.org/10.1103/PhysRevE.69.066138]
[137]
Kiani NA, Zenil H, Olczak J, et al. Evaluating network inference methods in terms of their ability to preserve the topology and complexity of genetic networks. Semin Cell Dev Biol 2016; 51: 44-52.
[http://dx.doi.org/10.1016/j.semcdb.2016.01.012]
[138]
Tang D, Wang M, Zheng W, et al. RapidMic: rapid computation of the maximal information coefficient. Evol Bioinform 2014; 10: 11-6.
[http://dx.doi.org/10.4137/EBO.S13121]
[139]
Zuo Y, Yu G, Tadesse MG, et al. Biological network inference using low order partial correlation. Methods 2014; 69(3): 266-73.
[http://dx.doi.org/10.1016/j.ymeth.2014.06.010]
[140]
Zhang R, Ren Z, Chen W. SILGGM: an extensive R package for efficient statistical inference in large-scale gene networks. PLOS Comput Biol 2018; 14(8)e1006369
[http://dx.doi.org/10.1371/journal.pcbi.1006369]
[141]
De la Fuente A, Bing N, Hoeschele I, et al. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 2004; 20(18): 3565-74.
[http://dx.doi.org/10.1093/bioinformatics/bth445]
[142]
Castelo R, Roverato A. A robust procedure for Gaussian graphical model search from microarray data with p larger than n. J Mach Learn Res 2006; 7: 2621-50.
[143]
Sulaimanov N, Koeppl H. Graph reconstruction using covariance-based methods. EURASIP J Bioinform Syst Biol 2016; 2016(1): 19.
[http://dx.doi.org/10.1186/s13637-016-0052-y]
[144]
Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods 2018; 23(4): 617-34.
[http://dx.doi.org/10.1037/met0000167]
[145]
Yu X, Zeng T, Wang X, et al. Unravelling personalized dysfunctional gene network of complex diseases based on differential network model. J Transl Med 2015; 13: 189.
[http://dx.doi.org/10.1186/s12967-015-0546-5]
[146]
Jiang X, Zhang H, Quan X. Differentially co-expressed disease gene identification based on gene co-expression network. BioMed Res Int 2016; 20163962761
[147]
Hsu C-L, Juan HF, Huang HC. Functional analysis and characterization of differential co-expression networks. Sci Rep 2015; 5: 13295.
[http://dx.doi.org/10.1038/srep13295]
[148]
Gov E, Arga KY. Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer. Sci Rep 2017; 7: 4996.
[http://dx.doi.org/10.1038/s41598-017-05298-w]
[149]
Zhu L, Ding Y, Chen C. MetaDCN: meta-analysis framework for differential co-expression network detection with an application in breast cancer. Bioinformatics 2017; 33(8): 1121-9.
[150]
Yu W, Zhao S, Wang Y, et al. Identification of cancer prognosis-associated functional modules using differential co-expression networks. Oncotarget 2017; 8(68): 112928-41.
[http://dx.doi.org/10.18632/oncotarget.22878]
[151]
Shi Z, Drow CK, Zhang B. Co-expression module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression. BMC Syst Biol 2010; 4: 74.
[http://dx.doi.org/10.1186/1752-0509-4-74]
[152]
Tian F, Zhao J, Fan X, et al. Weighted gene co-expression network analysis in identification of metastasis related genes of lung squamous cell carcinoma based on the cancer genome atlas database. J Thorac Dis 2017; 9(1): 42-53.
[http://dx.doi.org/10.21037/jtd.2017.01.04]
[153]
Chen J, Wang X, Hu B, et al. Candidate genes in gastric cancer identified by constructing a weighted gene co-expression network. PeerJ 2018; 6e4692
[http://dx.doi.org/10.7717/peerj.4692]
[154]
Tang J, Kong D, Cui Q, et al. Prognostic genes of breast cancer identified by gene co-expression network analysis. Front Oncol 2018; 8: 374.
[http://dx.doi.org/10.3389/fonc.2018.00374]
[155]
Boyle EI, Weng S, Gollub J, et al. GO:TermFinder-open source software for accessing gene ontology information and finding significantly enriched gene ontology terms associated with a list of genes. Bioinformatics 2004; 20(18): 3710-5.
[http://dx.doi.org/10.1093/bioinformatics/bth456]
[156]
Tsui IFL, Chari R, Buys TPH, et al. Public databases and software for the pathway analysis of cancer genomes. Cancer Inform 2007; 3: 379-97.
[http://dx.doi.org/10.1177/117693510700300027]
[157]
Subramaniana A, Tamayoa P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 2005; 102(43): 15545-50.
[http://dx.doi.org/10.1073/pnas.0506580102]
[158]
Zwiener I, Blettner M, Hommel G. Survival analysis-part 15 of a series on evaluation of scientific publications. Dtsch Arztebl Int 2011; 108(10): 163-9.
[159]
Ferreira JC, Patino CM. What is survival analysis, and when should I use it? J Bras Pneumol. 2016; 42(1): 77-77. [173] Kartsonaki C, Survival analysis. Diagn Histopathol 2016; 22(7): 263-70.
[160]
Bewick V, Cheek L, Ball J. Statistics review 12: survival analysis. Crit Care 2004; 8: 389-94.
[http://dx.doi.org/10.1186/cc2955]
[161]
Konganti K, Wang G, Yang E, et al. SBEToolbox: a Matlab Toolbox for biological network analysis. Evol Bioinform 2013; 9: 355-62.
[http://dx.doi.org/10.4137/EBO.S12012]
[162]
Albert R, Barabási A-L. Topology of evolving networks: local events and universality. Phys Rev Lett 2000; 85(24): 5234-7.
[http://dx.doi.org/10.1103/PhysRevLett.85.5234]
[163]
Poncela J, Gòmez-Gardeńes J, Florìa LM, et al. Complex cooperative networks from evolutionary preferential attachment. PLoS One 2008; 3(6)e2449
[http://dx.doi.org/10.1371/journal.pone.0002449]
[164]
Yamada T, Bork P. Evolution of biomolecular networks-lessons from metabolic and protein interactions. Natl Rev 2009; 10: 791-803.
[http://dx.doi.org/10.1038/nrm2787]
[165]
Teichmann SA, Babu MM. Gene regulatory network growth by duplication. Nat Genet 2004; 36(5): 492-6.
[http://dx.doi.org/10.1038/ng1340]
[166]
Yi S, Lin S, Li Y, et al. Functional variomics and network perturbation: connecting genotype to phenotype in cancer. Nat Rev Genet 2017; 18: 395-410.
[http://dx.doi.org/10.1038/nrg.2017.8]
[167]
Kim J, Kim I, Han SK, et al. Network rewiring is an important mechanism of gene essentiality change. Sci Rep 2012; 2: 900.
[http://dx.doi.org/10.1038/srep00900]
[168]
Dorogovtsev SN, Mendes JFF, Samukhin AN. Structure of growing networks with preferential linking. Phys Rev Lett 2000; 85(21): 4633-6.
[http://dx.doi.org/10.1103/PhysRevLett.85.4633]
[169]
Jeong H, Néda Z, Barabási AL. Measuring preferential attachment in evolving networks. Europhys Lett 2003; 61(4): 567-72.
[http://dx.doi.org/10.1209/epl/i2003-00166-9]
[170]
Pastor-Satorras R, Smith E, Solé RV. Evolving protein interaction networks through gene duplication. J Theor Biol 2003; 222: 199-210.
[http://dx.doi.org/10.1016/S0022-5193(03)00028-6]
[171]
Lindquist J, Ma J, Van den Driessche P, et al. Network evolution by different rewiring schemes. Physica D 2009; 238: 370-8.
[http://dx.doi.org/10.1016/j.physd.2008.10.016]
[172]
Moore C, Ghoshal G, Newman MEJ. Exact solutions for models of evolving networks with addition and deletion of nodes. Phys Rev E Stat Nonlin Soft Matter Phys 2006; 74036121
[http://dx.doi.org/10.1103/PhysRevE.74.036121]
[173]
Proulx SR, Promislow DEL, Phillips PC. Network thinking in ecology and evolution. Trends Ecol Evol 2005; 20(6): 345-53.
[http://dx.doi.org/10.1016/j.tree.2005.04.004]
[174]
Kim H, Anderson R. An experimental evaluation of robustness of networks. IEEE Syst J 2013; 7(2): 179-88.
[http://dx.doi.org/10.1109/JSYST.2012.2221851]
[175]
Dorogovtsev SN, Mendes JFF. Evolution of networks. Adv Phys 2002; 51: 1079.
[http://dx.doi.org/10.1080/00018730110112519]
[176]
Holme P, Kim BJ, Yoon CN, Han SK. Attack vulnerability of complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 2002; 65(5 Pt 2)056109
[http://dx.doi.org/10.1103/PhysRevE.65.056109]
[177]
Motter AE, Nishikawa T, Lai Y-C. Range-based attack on links in scale-free networks: are long-range links responsible for the small-world phenomenon? Phys Rev E Stat Nonlin Soft Matter Phys 2002; 66065103
[http://dx.doi.org/10.1103/PhysRevE.66.065103]
[178]
Motter AE. Cascade control and defense in complex networks. Phys Rev Lett 2004; 93098701
[http://dx.doi.org/10.1103/PhysRevLett.93.098701]
[179]
Quayle AP, Siddiqui ASJM, Jones S. Perturbation of Interaction Networks for Application to Cancer Therapy. Cancer Inform 2007; 5: 45-65.
[http://dx.doi.org/10.1177/117693510700500005]
[180]
Sun L, Wang S, Li K, et al. Analysis of cascading failure in gene networks. Front Genet 2012; 3: 292.
[http://dx.doi.org/10.3389/fgene.2012.00292]
[181]
Watts DJ. Small Worlds. Princeton: Princeton University Press 1999.
[http://dx.doi.org/10.1515/9780691188331]
[182]
Gong B, Liu J, Huang L, et al. Range-based attacks on links in random scale-free networks J Stat Mech 2008.P02008
[http://dx.doi.org/10.1088/1742-5468/2008/02/P02008]
[183]
Alenazi MJF, Sterbenz JPG. Evaluation and comparison of several graph robustness metrics to improve network resilience. 7th International Workshop on Reliable Networks Design and Modeling (RNDM).
[http://dx.doi.org/10.1109/RNDM.2015.7324302]
[184]
Roukny T, Bersini H, Pirotte H, et al. Default cascades in complex networks: topology and systemic risk. Sci Rep 2013; 3: 2759.
[http://dx.doi.org/10.1038/srep02759]
[185]
Watts DJ. A simple model of global cascades on random networks. Proc Natl Acad Sci USA 2002; 99(9): 5766-71.
[http://dx.doi.org/10.1073/pnas.082090499]
[186]
Crucitti P, Latora V, Marchiori M. Model for cascading failures in complex networks. Phys Rev 2004.E69045104
[187]
Lai Y-C, Motter AE, Nishikawa T. Attacks and cascades in complex networks. Lect Notes Phys 2004; 650: 299-310.
[http://dx.doi.org/10.1007/978-3-540-44485-5_14]
[188]
Majdandzic A, Podobnik B, Buldyrev SV, et al. Spontaneous recovery in dynamical networks. Nat Phys 2014; 10: 34-8.
[http://dx.doi.org/10.1038/nphys2819]
[189]
Shang Y. Localized recovery of complex networks against failure. Sci Rep 2016; 6: 30521.
[http://dx.doi.org/10.1038/srep30521]
[190]
Paulsson J. Summing up the noise in gene networks. Nature 2004; 427: 415-8.
[http://dx.doi.org/10.1038/nature02257]
[191]
Swain PS, Elowitz MB, Siggia ED. Intrinsic and extrinsic contributions to stochasticity in gene expression. Proc Natl Acad Sci USA 2002; 99(20): 12795-800.
[http://dx.doi.org/10.1073/pnas.162041399]
[192]
Kerr MK, Martin M, Churchill GA. Analysis of variance for gene expression microarray data. J Comput Biol 2000; 7(6): 819-37.
[http://dx.doi.org/10.1089/10665270050514954]
[193]
Pedraza JM, Van Oudenaarden A. Noise propagation in gene networks. Science 2005; 307(5717): 1965-9.
[http://dx.doi.org/10.1126/science.1109090]
[194]
Lestas I, Paulsson J, Ross NE, et al. Noise in gene regulatory networks special issue on systems biology. IEEE 2008; pp. 189-200.
[195]
Yambartsev A, Perlin MA, Kovchegov Y, et al. Unexpected links reflect the noise in networks. Biol Direct 2016; 11: 52.
[http://dx.doi.org/10.1186/s13062-016-0155-0]
[196]
Novoradovskaya N, Whitfield ML, Basehore LS, et al. Universal reference RNA as a standard for microarray experiments. BMC Genomics 2004; 5: 20.
[http://dx.doi.org/10.1186/1471-2164-5-20]
[197]
Balling F-He-R. Zeng AP. Reverse engineering and verification of gene networks: principles, assumptions, and limitations of present methods and future perspectives. J Biotechnol 2009; 144(3): 190-203.
[http://dx.doi.org/10.1016/j.jbiotec.2009.07.013]
[198]
Marbach D, Prill RJ, Schaffter T, et al. Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci USA 2010; 107(14): 6286-91.
[http://dx.doi.org/10.1073/pnas.0913357107]
[199]
Gillis J, Pavlidis P. Guilt by association is the exception rather than the rule in gene networks. PLOS Comput Biol 2012; 8(3)e1002444
[http://dx.doi.org/10.1371/journal.pcbi.1002444]
[200]
Uygun S, Peng C, Lehti-Shiu MD, et al. Utility and limitations of using gene expression data to identify functional associations. PLOS Comput Biol 2016; 12(12)e1005244
[http://dx.doi.org/10.1371/journal.pcbi.1005244]
[201]
Schulze L, Yuneva M. The big picture: exploring the metabolic cross-talk in cancer. Dis Model Mech 2018; 11(8)dmm036673
[http://dx.doi.org/10.1242/dmm.036673]
[202]
Butte J, Tamayo P, Slonim D, et al. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci USA 2000; 97(22): 12182-6.
[http://dx.doi.org/10.1073/pnas.220392197]
[203]
Moriyama M, Hoshida Y, Otsuka M, et al. Relevance network between chemo sensitivity and transcriptome in human hepatoma cells. Mol Cancer Ther 2003; 2: 199-205.
[204]
Ma S, Shi M, Li Y, et al. Incorporating gene co-expression network in identification of cancer prognosis markers. BMC Bioinformatics 2010; 11(271)
[http://dx.doi.org/10.1186/1471-2105-11-271]
[205]
Zhang J, Lu K, Xiang Y, et al. Weighted frequent gene co-expression network mining to identify genes involved in genome stability. PLOS Comput Biol 2012; 8(8)e1002656
[http://dx.doi.org/10.1371/journal.pcbi.1002656]
[206]
Udyavar AR, Hoeksema MD, Clark JE, et al. Co-expression network analysis identifies Spleen Tyrosine Kinase (SYK) as a candidate oncogenic driver in a subset of small-cell lung cancer. BMC Syst Biol 2013; 7(Suppl. 5): S1.
[http://dx.doi.org/10.1186/1752-0509-7-S5-S1]
[207]
Yang Y, Han L, Yuan Y, et al. Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nat Commun 2014; 5: 3231.
[http://dx.doi.org/10.1038/ncomms4231]
[208]
Chou W-C, Cheng A-L, Brotto M, et al. Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer. BMC Genomics 2014; 15: 300.
[http://dx.doi.org/10.1186/1471-2164-15-300]
[209]
Deng S-P, Zhu L. Huang De-S. Mining the bladder cancer-associated genes by an integrated strategy for the construction and analysis of differential co-expression networks. BMC Genomics 2015; 16(Suppl. 3): S4.
[http://dx.doi.org/10.1186/1471-2164-16-S3-S4]
[210]
Deng S-P, Zhu L, Huang D-S. Predicting hub genes associated with cervical cancer through gene co-expression networks. IEEE/ACM Trans Comput Biol Bioinform 2016; 13(1): 27-35.
[http://dx.doi.org/10.1109/TCBB.2015.2476790]
[211]
Yue Z, Li H-T, Yang Y, et al. Identification of breast cancer candidate genes using gene co-expression and protein-protein interaction information. Oncotarget 2016; 7(24): 36092-100.
[http://dx.doi.org/10.18632/oncotarget.9132]
[212]
Chen P, Wang F, Feng J, et al. Co-expression network analysis identified six hub genes in association with metastasis risk and prognosis in hepatocellular carcinoma. Oncotarget 2017; 8(30): 48948-58.
[http://dx.doi.org/10.18632/oncotarget.16896]
[213]
Zhang T, Wang X, Yue Z. Identification of candidate genes related to pancreatic cancer based on analysis of gene co-expression and protein-protein interaction network. Oncotarget 2017; 8(41): 71105-16.
[http://dx.doi.org/10.18632/oncotarget.20537]
[214]
Langfelder P, Mischel PS, Horvath S. When is hub gene selection better than standard meta-analysis? PLoS One 2013; 8(4)e61505
[http://dx.doi.org/10.1371/journal.pone.0061505]
[215]
Langfelder P, Zhang B, Horvath S. Defining clusters from a hierarchical cluster tree: the dynamic tree cut library for R. Bioinformatics 2008; 24(5): 719-20.
[http://dx.doi.org/10.1093/bioinformatics/btm563]
[216]
Horvath S, Dong J. Geometric interpretation of gene co-expression network analysis. PLOS Comput Biol 2008; 4(8)e1000117
[http://dx.doi.org/10.1371/journal.pcbi.1000117]
[217]
Langfelder P, Luo R, Oldham MC, Horvath S. Is my network module preserved and reproducible? PLOS Comput Biol 2011; 7(1)e1001057
[http://dx.doi.org/10.1371/journal.pcbi.1001057]
[218]
Apostolou P, Iliopoulos AC, Parsonidis P, et al. Gene expression profiling as a potential predictor between normal and cancer samples in gastrointestinal carcinoma. Oncotarget 2019; 10(36): 3328-38.
[http://dx.doi.org/10.18632/oncotarget.26913]
[219]
Perkins AD, Langston MA. Threshold selection in gene co-expression networks using spectral graph theory techniques. BMC Bioinformatics 2009; 10(11): S4.
[http://dx.doi.org/10.1186/1471-2105-10-S11-S4]
[220]
Wolfram Research, Inc.. Mathematica, Version 112, Champaign, IL In: 2017.
[221]
Wang E. Cancer systems biology. CRC Press 2017; p. 456.
[222]
Saelens W, Cannoodt R, Saeys Y. A comprehensive evaluation of module detection methods for gene expression data. Nat Commun 2018; 9: 1090.
[http://dx.doi.org/10.1038/s41467-018-03424-4]
[223]
Yan J, Risacher SL, Shen L, et al. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Brief Bioinform 2018; 19(6): 1370-81.

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