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From Gene Expression to Disease Phenotypes: Network-Based Approaches to Study Complex Human Diseases

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Transcriptomics and Gene Regulation

Part of the book series: Translational Bioinformatics ((TRBIO,volume 9))

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Abstract

Gene expression is a fundamental biological process under tight regulation at all levels in normal cells. Its dysregulation can cause abnormal cell behaviors and result in diseases, and thus gene expression profiling and analysis have been widely used to provide the first clue about the molecular mechanisms of human diseases. Because genes and their products interact with and regulate one another, it is essential to analyze gene expression data and understand the genetics of disease in a biological network context. In this chapter, we first introduce the state-of-the-art gene expression analysis (GEA) with network integration and the joint analysis of mRNA and miRNA expression to understand disease regulatory mechanisms and then discuss how disease genes are predicted by incorporating knowledge of gene regulation and characterized in biological networks.

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References

  1. Manel E, Paul C, Stephen B, James H. A gene hypermethylation profile of human cancer. Cancer Res. 2001;61:3225–9.

    Google Scholar 

  2. Jones PA, Baylin SB. The fundamental role of epigenetic events in cancer. Nat Rev Genet. 2002;3:415–28.

    Article  CAS  PubMed  Google Scholar 

  3. Darnell JE Jr. Transcription factors as targets for cancer therapy. Nat Rev Cancer. 2002;2:740–9.

    Article  CAS  PubMed  Google Scholar 

  4. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144:646–74.

    Article  CAS  PubMed  Google Scholar 

  5. Herz HM, Hu D, Shilatifard A. Enhancer malfunction in cancer. Mol Cell. 2014;53:859–66.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  6. Croce CM. Causes and consequences of microRNA dysregulation in cancer. Nat Rev Genet. 2009;10:704–14.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  7. Akavia UD, Litvin O, Kim J, Sanchez-Garcia F, Kotliar D, et al. An integrated approach to uncover drivers of cancer. Cell. 2010;143:1005–17.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  8. Kim YA, Wuchty S, Przytycka TM. Identifying causal genes and dysregulated pathways in complex diseases. PLoS Comput Biol. 2011;7:e1001095.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  9. Herranz H, Cohen SM. MicroRNAs and gene regulatory networks: managing the impact of noise in biological systems. Genes Dev. 2010;24:1339–44.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  10. Tsang J, Zhu J, van Oudenaarden A. MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol Cell. 2007;26:753–67.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Stark A, Brennecke J, Bushati N, Russell RB, Cohen SM. Animal MicroRNAs confer robustness to gene expression and have a significant impact on 3’UTR evolution. Cell. 2005;123:1133–46.

    Article  CAS  PubMed  Google Scholar 

  12. Lu M, Zhang Q, Deng M, Miao J, Guo Y, et al. An analysis of human microRNA and disease associations. PLoS ONE. 2008;3:e3420.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  13. Ramos RG, Olden K. Gene-environment interactions in the development of complex disease phenotypes. Int J Environ Res Public Health. 2008;5:4–11.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  14. Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33:D514–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  15. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273:1516–7.

    Article  CAS  PubMed  Google Scholar 

  16. Mayeux R. Mapping the new frontier: complex genetic disorders. J Clin Invest. 2005;115:1404–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  17. Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past successes for mendelian disease, future approaches for complex disease. Nat Genet. 2003;33(Suppl):228–37.

    Article  CAS  PubMed  Google Scholar 

  18. Ritchie GR, Dunham I, Zeggini E, Flicek P. Functional annotation of noncoding sequence variants. Nat Methods. 2014;11:294–6.

    Article  CAS  PubMed  Google Scholar 

  19. Raney BJ, Cline MS, Rosenbloom KR, Dreszer TR, Learned K, et al. ENCODE whole-genome data in the UCSC genome browser (2011 update). Nucleic Acids Res. 2011;39:D871–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  20. Li C, Li H. Network-constrained regularization and variable selection for analysis of genomic data. Bioinformatics. 2008;24:1175–82.

    Article  CAS  PubMed  Google Scholar 

  21. Zhang W, Wan YW, Allen GI, Pang K, Anderson ML, et al. Molecular pathway identification using biological network-regularized logistic models. BMC Genom. 2013;14(Suppl 8):S7.

    Article  Google Scholar 

  22. Wu C, Zhu J, Zhang X. Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma. BMC Bioinform. 2013;14:365.

    Article  CAS  Google Scholar 

  23. 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.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  24. Glaab E, Baudot A, Krasnogor N, Schneider R, Valencia A. EnrichNet: network-based gene set enrichment analysis. Bioinformatics. 2012;28:i451–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  25. Xia J, Gill EE, Hancock RE. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 2015;10:823–44.

    Article  CAS  PubMed  Google Scholar 

  26. Fryer RM, Randall J, Yoshida T, Hsiao LL, Blumenstock J, et al. Global analysis of gene expression: methods, interpretation, and pitfalls. Exp Nephrol. 2002;10:64–74.

    Article  CAS  PubMed  Google Scholar 

  27. Lemetre C, Zhang Q, Zhang ZD. SubNet: a Java application for subnetwork extraction. Bioinformatics. 2013;29:2509–11.

    Article  PubMed  CAS  Google Scholar 

  28. Marko NF, Weil RJ. Mathematical modeling of molecular data in translational medicine: theoretical considerations. Sci Transl Med. 2010;2:56rv54.

    Article  Google Scholar 

  29. Wan YW, Nagorski J, Allen GI, Li ZH, Liu ZD. Identifying cancer biomarkers through a network regularized Cox model. In: Genomic Signal Processing and Statistics (GENSIPS), 2013 IEEE international workshop on IEEE. Houston, TX, 2013; pp. 36–39.

    Google Scholar 

  30. Wang Y, Klijn JG, Zhang Y, Sieuwerts AM, Look MP, et al. Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer. Lancet. 2005;365:671–9.

    Article  CAS  PubMed  Google Scholar 

  31. van’t Veer LJ, Dai HY, van de Vijver MJ, He YDD, Hart AAM, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature. 2002;415:530–6.

    Article  Google Scholar 

  32. Parker JS, Mullins M, Cheang MC, Leung S, Voduc D, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27:1160–7.

    Article  PubMed Central  PubMed  Google Scholar 

  33. Atias N, Istrail S, Sharan R. Pathway-based analysis of genomic variation data. Curr Opin Genet Dev. 2013;23:622–6.

    Article  CAS  PubMed  Google Scholar 

  34. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140.

    Article  PubMed Central  PubMed  Google Scholar 

  35. Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, et al. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010;38:W214–20.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  36. Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, et al. A travel guide to Cytoscape plugins. Nat Methods. 2012;9:1069–76.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  37. Lee RC, Feinbaum RL, Ambros V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell. 1993;75:843–54.

    Article  CAS  PubMed  Google Scholar 

  38. Zhao Y, Ransom JF, Li A, Vedantham V, von Drehle M, et al. Dysregulation of cardiogenesis, cardiac conduction, and cell cycle in mice lacking miRNA-1-2. Cell. 2007;129:303–17.

    Article  CAS  PubMed  Google Scholar 

  39. Trang P, Wiggins JF, Daige CL, Cho C, Omotola M, et al. Systemic delivery of tumor suppressor microRNA mimics using a neutral lipid emulsion inhibits lung tumors in mice. Mol Ther. 2011;19:1116–22.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  40. Wahlquist C, Jeong D, Rojas-Munoz A, Kho C, Lee A, et al. Inhibition of miR-25 improves cardiac contractility in the failing heart. Nature. 2014;508:531–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  41. Ludwig N, Nourkami-Tutdibi N, Backes C, Lenhof HP, Graf N, et al. Circulating serum miRNAs as potential biomarkers for nephroblastoma. Pediatr Blood Cancer. 2015;62:1360–1367.

    Google Scholar 

  42. van Schooneveld E, Wildiers H, Vergote I, Vermeulen PB, Dirix LY, et al. Dysregulation of microRNAs in breast cancer and their potential role as prognostic and predictive biomarkers in patient management. Breast Cancer Res. 2015;17:526.

    Google Scholar 

  43. Knezevic J, Pfefferle AD, Petrovic I, Greene SB, Perou CM, et al. Expression of miR-200c in claudin-low breast cancer alters stem cell functionality, enhances chemosensitivity and reduces metastatic potential. Oncogene. 2015; doi:10.1038/onc.2015.48.

    Google Scholar 

  44. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. Bmc Bioinform. 2008;9:559.

    Article  CAS  Google Scholar 

  45. McKinney-Freeman S, Cahan P, Li H, Lacadie SA, Huang HT, et al. The transcriptional landscape of hematopoietic stem cell ontogeny. Cell Stem Cell. 2012;11:701–14.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  46. Miller JA, Ding SL, Sunkin SM, Smith KA, Ng L, et al. Transcriptional landscape of the prenatal human brain. Nature. 2014;508:199.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  47. Okamura Y, Aoki Y, Obayashi T, Tadaka S, Ito S, et al. COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems. Nucleic Acids Res. 2015;43:D82–6.

    Article  PubMed Central  PubMed  Google Scholar 

  48. Marbach D, Costello JC, Kuffner R, Vega NM, Prill RJ, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012;9:796.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007;5:54–66.

    Article  CAS  Google Scholar 

  50. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P. Inferring regulatory networks from expression data using tree-based methods. PLoS One. 2010;5:e12776.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  51. Salgado H, Peralta-Gil M, Gama-Castro S, Santos-Zavaleta A, Muniz-Rascado L, et al. RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more. Nucleic Acids Res. 2013;41:D203–13.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  52. Jiang J, Gusev Y, Aderca I, Mettler TA, Nagorney DM, et al. Association of MicroRNA expression in hepatocellular carcinomas with hepatitis infection, cirrhosis, and patient survival. Clin Cancer Res. 2008;14:419–27.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  53. Jopling CL, Yi M, Lancaster AM, Lemon SM, Sarnow P. Modulation of hepatitis C virus RNA abundance by a liver-specific MicroRNA. Science. 2005;309:1577–81.

    Article  CAS  PubMed  Google Scholar 

  54. Wang X, Zhang X, Ren XP, Chen J, Liu H, et al. MicroRNA-494 targeting both proapoptotic and antiapoptotic proteins protects against ischemia/reperfusion-induced cardiac injury. Circulation. 2010;122:1308–18.

    Article  PubMed Central  PubMed  Google Scholar 

  55. Xu J, Hu Z, Xu Z, Gu H, Yi L, et al. Functional variant in microRNA-196a2 contributes to the susceptibility of congenital heart disease in a Chinese population. Hum Mutat. 2009;30:1231–6.

    Article  CAS  PubMed  Google Scholar 

  56. Abelson JF, Kwan KY, O’Roak BJ, Baek DY, Stillman AA, et al. Sequence variants in SLITRK1 are associated with Tourette’s syndrome. Science. 2005;310:317–20.

    Article  CAS  PubMed  Google Scholar 

  57. Yang F, Wang W, Zhou C, Xi W, Yuan L, et al. MiR-221/222 promote human glioma cell invasion and angiogenesis by targeting TIMP2. Tumour Biol. 2015;36:3763.

    Article  CAS  PubMed  Google Scholar 

  58. Zhao S, Yao D, Chen J, Ding N, Ren F. MiR-20a promotes cervical cancer proliferation and metastasis in vitro and in vivo. PLoS ONE. 2015;10:e0120905.

    Article  PubMed Central  PubMed  Google Scholar 

  59. Houbaviy HB, Murray MF, Sharp PA. Embryonic stem cell-specific MicroRNAs. Dev Cell. 2003;5:351–8.

    Article  CAS  PubMed  Google Scholar 

  60. Enright AJ, John B, Gaul U, Tuschl T, Sander C, et al. MicroRNA targets in Drosophila. Genome Biol. 2003;5:R1.

    Article  PubMed Central  PubMed  Google Scholar 

  61. Kertesz M, Iovino N, Unnerstall U, Gaul U, Segal E. The role of site accessibility in microRNA target recognition. Nat Genet. 2007;39:1278–84.

    Article  CAS  PubMed  Google Scholar 

  62. Thadani R, Tammi MT. MicroTar: predicting microRNA targets from RNA duplexes. BMC Bioinform. 2006;7(Suppl 5):S20.

    Article  CAS  Google Scholar 

  63. Stingo FC, Chen YA, Vannucci M, Barrier M, Mirkes PE. A Bayesian graphical modeling approach to microRNA regulatory network inference. Ann Appl Stat. 2010;4:2024–48.

    Article  PubMed Central  PubMed  Google Scholar 

  64. Tabas-Madrid D, Muniategui A, Sanchez-Caballero I, Martinez-Herrera DJ, Sorzano CO, et al. Improving miRNA-mRNA interaction predictions. BMC Genom. 2014;15(Suppl 10):S2.

    Article  Google Scholar 

  65. Ritchie W, Flamant S, Rasko JE. Predicting microRNA targets and functions: traps for the unwary. Nat Methods. 2009;6:397–8.

    Article  CAS  PubMed  Google Scholar 

  66. Dweep H, Sticht C, Pandey P, Gretz N. miRWalk–database: prediction of possible miRNA binding sites by “walking” the genes of three genomes. J Biomed Inform. 2011;44:839–47.

    Article  CAS  PubMed  Google Scholar 

  67. Xiao F, Zuo Z, Cai G, Kang S, Gao X, et al. miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 2009;37:D105–10.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  68. Huang JC, Babak T, Corson TW, Chua G, Khan S, et al. Using expression profiling data to identify human microRNA targets. Nat Methods. 2007;4:1045–9.

    Article  CAS  PubMed  Google Scholar 

  69. Joung JG, Hwang KB, Nam JW, Kim SJ, Zhang BT. Discovery of microRNA-mRNA modules via population-based probabilistic learning. Bioinformatics. 2007;23:1141–7.

    Article  CAS  PubMed  Google Scholar 

  70. Muniategui A, Nogales-Cadenas R, Vazquez M, Aranguren XL, Agirre X, et al. Quantification of miRNA-mRNA interactions. PLoS ONE. 2012;7:e30766.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  71. Tran DH, Satou K, Ho TB. Finding microRNA regulatory modules in human genome using rule induction. BMC Bioinform. 2008;9(Suppl 12):S5.

    Article  CAS  Google Scholar 

  72. Bleazard T, Lamb JA, Griffiths-Jones S Bias in microRNA functional enrichment analysis. Bioinformatics. 2015;31:1592–1598.

    Google Scholar 

  73. Gusev Y, Schmittgen TD, Lerner M, Postier R, Brackett D. Computational analysis of biological functions and pathways collectively targeted by co-expressed microRNAs in cancer. BMC Bioinform. 2007;8(Suppl 7):S16.

    Article  CAS  Google Scholar 

  74. Liu B, Li J, Cairns MJ. Identifying miRNAs, targets and functions. Brief Bioinform. 2014;15:1–19.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  75. Welter D, MacArthur J, Morales J, Burdett T, Hall P, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Res. 2014;42:D1001–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  76. Schizophrenia Working Group of the Psychiatric Genomics C. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.

    Article  CAS  Google Scholar 

  77. Nicolae DL, Gamazon E, Zhang W, Duan S, Dolan ME, et al. Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS. PLoS Genet. 2010;6:e1000888.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  78. Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, et al. An atlas of active enhancers across human cell types and tissues. Nature. 2014;507:455–61.

    Article  CAS  PubMed  Google Scholar 

  79. Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science. 2012;337:1190–5.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  80. Hou L, Zhao H. A review of post-GWAS prioritization approaches. Front Genet. 2013;4:280.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  81. Thurman RE, Rynes E, Humbert R, Vierstra J, Maurano MT, et al. The accessible chromatin landscape of the human genome. Nature. 2012;489:75–82.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  82. Sullivan PF, Lin D, Tzeng JY, van den Oord E, Perkins D, et al. Genomewide association for schizophrenia in the CATIE study: results of stage 1. Mol Psychiatry. 2008;13:570–84.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  83. Kelemen O, Kovacs T, Keri S. Contrast, motion, perceptual integration, and neurocognition in schizophrenia: the role of fragile-X related mechanisms. Prog Neuropsychopharmacol Biol Psychiatry. 2013;46:92–7.

    Article  PubMed  Google Scholar 

  84. Bauer-Mehren A, Bundschus M, Rautschka M, Mayer MA, Sanz F, et al. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. Plos One. 2011;6:e20284.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  85. Rappaport N, Nativ N, Stelzer G, Twik M, Guan-Golan Y, et al. MalaCards: an integrated compendium for diseases and their annotation. Database J Biol Databases Curation. 2013;bat018.

    Google Scholar 

  86. Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 2011;12:56–68.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  87. 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. 2011;7:e1001273.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  88. Ideker T, Sharan R. Protein networks in disease. Genome Res. 2008;18:644–52.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  89. Goh KI, Cusick ME, Valle D, Childs B, Vidal M, et al. The human disease network. Proc Natl Acad Sci USA. 2007;104:8685–90.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  90. Jonsson PF, Bates PA. Global topological features of cancer proteins in the human interactome. Bioinformatics. 2006;22:2291–7.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  91. Sun JC, Zhao ZM. A comparative study of cancer proteins in the human protein-protein interaction network. Bmc Genomics 2010;11.

    Google Scholar 

  92. Yang Y, Han L, Yuan Y, Li J, Hei NN, et al. Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types. Nat Commun 2014;5:3231.

    Google Scholar 

  93. Magger O, Waldman YY, Ruppin E, Sharan R. Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks. Plos Comput Biol 2012;8:e1002690.

    Google Scholar 

  94. Glaab E, Baudot A, Krasnogor N, Valencia A. TopoGSA: network topological gene set analysis. Bioinformatics. 2010;26:1271–2.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  95. Minguez P, Gotz S, Montaner D, Al-Shahrour F, Dopazo J. SNOW, a web-based tool for the statistical analysis of protein-protein interaction networks. Nucleic Acids Res. 2009;37:W109–14.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  96. Assenov Y, Ramirez F, Schelhorn SE, Lengauer T, Albrecht M. Computing topological parameters of biological networks. Bioinformatics. 2008;24:282–4.

    Article  CAS  PubMed  Google Scholar 

  97. Wang JG, Zhang SH, Wang Y, Chen LN, Zhang XS. Disease-aging network reveals significant roles of aging genes in connecting genetic diseases. Plos Comput Biology 2009;5:e1000521.

    Google Scholar 

  98. Glaab E, Baudot A, Krasnogor N, Valencia A. Extending pathways and processes using molecular interaction networks to analyse cancer genome data. Bmc Bioinform. 2010;11:597.

    Article  Google Scholar 

  99. Kozomara A, Griffiths-Jones S. miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Res. 2014;42:D68–73.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  100. Betel D, Wilson M, Gabow A, Marks DS, Sander C. The microRNA.org resource: targets and expression. Nucleic Acids Res. 2008;36:D149–53.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  101. Ru Y, Kechris KJ, Tabakoff B, Hoffman P, Radcliffe RA, et al. The multiMiR R package and database: integration of microRNA-target interactions along with their disease and drug associations. Nucleic Acids Res. 2014;42:e133.

    Article  PubMed Central  PubMed  CAS  Google Scholar 

  102. Jacobsen A, Silber J, Harinath G, Huse JT, Schultz N, et al. Analysis of microRNA-target interactions across diverse cancer types. Nat Struct Mol Biol. 2013;20:1325–32.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  103. Jiang Q, Wang Y, Hao Y, Juan L, Teng M, et al. miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res. 2009;37:D98–104.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  104. Purcell SM, Moran JL, Fromer M, Ruderfer D, Solovieff N, et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature. 2014;506:185–90.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  105. Xie B, Ding Q, Han H, Wu D. miRCancer: a microRNA-cancer association database constructed by text mining on literature. Bioinformatics. 2013;29:638–44.

    Article  CAS  PubMed  Google Scholar 

  106. Bhattacharya A, Ziebarth JD, Cui Y. PolymiRTS Database 3.0: linking polymorphisms in microRNAs and their target sites with human diseases and biological pathways. Nucleic Acids Res. 2014;42:D86–91.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  107. Bruno AE, Li L, Kalabus JL, Pan Y, Yu A, et al. miRdSNP: a database of disease-associated SNPs and microRNA target sites on 3’UTRs of human genes. BMC Genom. 2012;13:44.

    Article  CAS  Google Scholar 

  108. Scardoni G, Petterlini M, Laudanna C. Analyzing biological network parameters with CentiScaPe. Bioinformatics. 2009;25:2857–9.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  109. Junker BH, Koschutzki D, Schreiber F. Exploration of biological network centralities with CentiBiN. Bmc Bioinform. 2006;7:219.

    Article  Google Scholar 

  110. Grassler J, Koschutzki D, Schreiber F. CentiLib: comprehensive analysis and exploration of network centralities. Bioinformatics. 2012;28:1178–9.

    Article  PubMed  CAS  Google Scholar 

  111. Hindorff LA MJEBI, Morales J (European Bioinformatics Institute), Junkins HA, Hall PN, Klemm AK, Manolio TA. (Available at: http://www.genome.gov/gwastudies). A catalog of published genome-wide association studies. Accessed 31 Mar 2015.

  112. Forbes SA, Beare D, Gunasekaran P, Leung K, Bindal N, et al. COSMIC: exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res. 2014;43:D805–11.

    Article  PubMed Central  PubMed  Google Scholar 

  113. Das J, Yu HY. HINT: High-quality protein interactomes and their applications in understanding human disease. BMC Syst Biol. 2012;6:92.

    Article  PubMed Central  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Genetics, Albert Einstein College of Medicine, Michael F. Price Center for Genetic and Translational Medicine, 1301 Morris Park Avenue, Bronx, NY, 10461, USA

    Quanwei Zhang, Wen Zhang, Rubén Nogales-Cadenas, Jhin-Rong Lin, Ying Cai & Zhengdong D. Zhang

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  1. Quanwei Zhang
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  1. Center for Stem Cell and Regenerati, The Vivian L. Smith Department of N, HOUSTON, USA

    Jiaqian Wu

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Zhang, Q., Zhang, W., Nogales-Cadenas, R., Lin, JR., Cai, Y., Zhang, Z.D. (2016). From Gene Expression to Disease Phenotypes: Network-Based Approaches to Study Complex Human Diseases. In: Wu, J. (eds) Transcriptomics and Gene Regulation . Translational Bioinformatics, vol 9. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7450-5_5

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