Browse > Article
http://dx.doi.org/10.5808/GI.2013.11.4.200

Review of Biological Network Data and Its Applications  

Yu, Donghyeon (Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center)
Kim, MinSoo (Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center)
Xiao, Guanghua (Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center)
Hwang, Tae Hyun (Department of Clinical Sciences, Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center)
Publication Information
Genomics & Informatics / v.11, no.4, 2013 , pp. 200-210 More about this Journal
Abstract
Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.
Keywords
biological network; disease gene prioritization; gene regulatory networks; genome-wide association study; protein function prediction; protein-protein interaction;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Salgado H, Peralta-Gil M, Gama-Castro S, Santos-Zavaleta A, Muñiz-Rascado L, García-Sotelo JS, et al. RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more. Nucleic Acids Res 2013;41:D203-D213.   DOI   ScienceOn
2 Schwikowski B, Uetz P, Fields S. A network of protein-protein interactions in yeast. Nat Biotechnol 2000;18:1257-1261.   DOI   ScienceOn
3 Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 2000;28:27-30.   DOI   ScienceOn
4 Joshi-Tope G, Gillespie M, Vastrik I, D'Eustachio P, Schmidt E, de Bono B, et al. Reactome: a knowledgebase of biological pathways. Nucleic Acids Res 2005;33:D428-D432.
5 Aittokallio T, Schwikowski B. Graph-based methods for analysing networks in cell biology. Brief Bioinform 2006;7: 243-255.   DOI   ScienceOn
6 Zhu X, Gerstein M, Snyder M. Getting connected: analysis and principles of biological networks. Genes Dev 2007;21: 1010-1024.   DOI   ScienceOn
7 Heller MJ. DNA microarray technology: devices, systems, and applications. Annu Rev Biomed Eng 2002;4:129-153.   DOI   ScienceOn
8 Lim J, Hao T, Shaw C, Patel AJ, Szabó G, Rual JF, et al. A protein- protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell 2006; 125:801-814.   DOI   ScienceOn
9 Kondor RI, Lafferty JD. Diffusion kernels on graphs and other discrete input spaces. In: ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning, 2002 Jul 8-12, Sydney. pp. 315-322.
10 Gandhi TK, Zhong J, Mathivanan S, Karthick L, Chandrika KN, Mohan SS, et al. Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat Genet 2006;38:285-293.   DOI   ScienceOn
11 Wood LD, Parsons DW, Jones S, Lin J, Sjöblom T, Leary RJ, et al. The genomic landscapes of human breast and colorectal cancers. Science 2007;318:1108-1113.   DOI   ScienceOn
12 Kourmpetis YA, van Dijk AD, Bink MC, van Ham RC, ter Braak CJ. Bayesian Markov Random Field analysis for protein function prediction based on network data. PLoS One 2010;5:e9293.   DOI   ScienceOn
13 Cherry JM, Adler C, Ball C, Chervitz SA, Dwight SS, Hester ET, et al. SGD: Saccharomyces Genome Database. Nucleic Acids Res 1998;26:73-79.   DOI   ScienceOn
14 Deng M, Tu Z, Sun F, Chen T. Mapping Gene Ontology to proteins based on protein-protein interaction data. Bioinformatics 2004;20:895-902.   DOI   ScienceOn
15 Collins SR, Kemmeren P, Zhao XC, Greenblatt JF, Spencer F, Holstege FC, et al. Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol Cell Proteomics 2007;6:439-450.   DOI
16 Nariai N, Kolaczyk ED, Kasif S. Probabilistic protein function prediction from heterogeneous genome-wide data. PLoS One 2007;2:e337.   DOI
17 Lee H, Tu Z, Deng M, Sun F, Chen T. Diffusion kernel-based logistic regression models for protein function prediction. OMICS 2006;10:40-55.   DOI   ScienceOn
18 Jiang X, Nariai N, Steffen M, Kasif S, Kolaczyk ED. Integration of relational and hierarchical network information for protein function prediction. BMC Bioinformatics 2008;9:350.   DOI   ScienceOn
19 Jiang X, Gold D, Kolaczyk ED. Network-based auto-probit modeling for protein function prediction. Biometrics 2011; 67:958-966.   DOI   ScienceOn
20 Lanckriet GR, De Bie T, Cristianini N, Jordan MI, Noble WS. A statistical framework for genomic data fusion. Bioinformatics 2004;20:2626-2635.   DOI   ScienceOn
21 Wang H, Huang H, Ding C. Function-function correlated multi-label protein function prediction over interaction networks. J Comput Biol 2013;20:322-343.   DOI   ScienceOn
22 Kirkpatrick S, Gelatt CD Jr, Vecchi MP. Optimization by simulated annealing. Science 1983;220:671-680.   DOI   ScienceOn
23 Li SZ. Markov Random Field Modeling in Computer Vision. Berlin: Springer-Verlag, 1995.
24 Vandenberghe L, Boyd S. Semidefinite programming. SIAM Rev 1996;38:49-95.   DOI   ScienceOn
25 Ito T, Tashiro K, Muta S, Ozawa R, Chiba T, Nishizawa M, et al. Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. Proc Natl Acad Sci U S A 2000;97:1143-1147.   DOI   ScienceOn
26 Hishigaki H, Nakai K, Ono T, Tanigami A, Takagi T. Assessment of prediction accuracy of protein function from protein: protein interaction data. Yeast 2001;18:523-531.   DOI   ScienceOn
27 Chua HN, Sung WK, Wong L. Exploiting indirect neighbours and topological weight to predict protein function from protein-protein interactions. Bioinformatics 2006;22: 1623-1630.   DOI   ScienceOn
28 Dennis G Jr, Sherman BT, Hosack DA, Yang J, Gao W, Lane HC, et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 2003;4:P3.   DOI
29 Cancer Genome Atals Network. Comprehensive molecular characterization of human colon and rectal cancer. Nature 2012;487:330-337.   DOI   ScienceOn
30 Cancer Genome Atlas Research Network, Kandoth C, Schultz N, Cherniack AD, Akbani R, Liu Y, et al. Integrated genomic characterization of endometrial carcinoma. Nature 2013;497:67-73.   DOI   ScienceOn
31 Fan J, Liao Y, Mincheva M. Large covariance estimation by thresholding principal orthogonal complements. J R Stat Soc Series B Stat Methodol 2013;75:603-680.   DOI   ScienceOn
32 Sangalli LM, Ramsay JO, Ramsay TO. Spatial spline regression models. J R Stat Soc Series B Stat Methodol 2013; 75:681-703.   DOI   ScienceOn
33 Sharan R, Ulitsky I, Shamir R. Network-based prediction of protein function. Mol Syst Biol 2007;3:88.
34 Hofree M, Shen JP, Carter H, Gross A, Ideker T. Networkbased stratification of tumor mutations. Nat Methods 2013; 10:1108-1115.   DOI   ScienceOn
35 Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 2011;474: 609-615.   DOI   ScienceOn
36 Cancer Genome Atals Network. Comprehensive molecular portraits of human breast tumours. Nature 2012;490:61-70.   DOI   ScienceOn
37 von Mering C, Krause R, Snel B, Cornell M, Oliver SG, Fields S, et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 2002;417:399-403.   DOI
38 Vazquez A, Flammini A, Maritan A, Vespignani A. Global protein function prediction from protein-protein interaction networks. Nat Biotechnol 2003;21:697-700.   DOI   ScienceOn
39 Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 2000; 25:25-29.   DOI   ScienceOn
40 Chi X, Hou J. An iterative approach of protein function prediction. BMC Bioinformatics 2011;12:437.   DOI
41 Karaoz U, Murali TM, Letovsky S, Zheng Y, Ding C, Cantor CR, et al. Whole-genome annotation by using evidence integration in functional-linkage networks. Proc Natl Acad Sci U S A 2004;101:2888-2893.   DOI   ScienceOn
42 Nabieva E, Jim K, Agarwal A, Chazelle B, Singh M. Whole-proteome prediction of protein function via graphtheoretic analysis of interaction maps. Bioinformatics 2005;21 Suppl 1:i302-i310.   DOI   ScienceOn
43 Tsuda K, Shin H, Schölkopf B. Fast protein classification with multiple networks. Bioinformatics 2005;21 Suppl 2:ii59-ii65.
44 Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function. Genome Biol 2008;9 Suppl 1:S4.
45 Wang J, Li Y. Sequential linear neighborhood propagation for semi-supervised protein function prediction. J Bioinform Comput Biol 2011;9:663-679.   DOI   ScienceOn
46 Moosavi S, Rahgozar M, Rahimi A. Protein function prediction using neighbor relativity in protein-protein interaction network. Comput Biol Chem 2013;43:11-16.   DOI   ScienceOn
47 Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 2006;7 Suppl 1:S7.
48 Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, et al. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 2007;5:e8.   DOI
49 Sayers EW, Barrett T, Benson DA, Bryant SH, Canese K, Chetvernin V, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2009; 37:D5-D15.   DOI   ScienceOn
50 Jia P, Zheng S, Long J, Zheng W, Zhao Z. dmGWAS: dense module searching for genome-wide association studies in protein-protein interaction networks. Bioinformatics 2011; 27:95-102.   DOI   ScienceOn
51 Barrett T, Suzek TO, Troup DB, Wilhite SE, Ngau WC, Ledoux P, et al. NCBI GEO: mining millions of expression profiles: database and tools. Nucleic Acids Res 2005;33:D562- D566.
52 Gollub J, Ball CA, Binkley G, Demeter J, Finkelstein DB, Hebert JM, et al. The Stanford Microarray Database: data access and quality assessment tools. Nucleic Acids Res 2003; 31:94-96.   DOI   ScienceOn
53 Hunter S, Apweiler R, Attwood TK, Bairoch A, Bateman A, Binns D, et al. InterPro: the integrative protein signature database. Nucleic Acids Res 2009;37:D211-D215.   DOI   ScienceOn
54 Ferrucci L, Bandinelli S, Benvenuti E, Di Iorio A, Macchi C, Harris TB, et al. Subsystems contributing to the decline in ability to walk: bridging the gap between epidemiology and geriatric practice in the InCHIANTI study. J Am Geriatr Soc 2000;48:1618-1625.   DOI
55 Cho YS, Go MJ, Kim YJ, Heo JY, Oh JH, Ban HJ, et al. A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits. Nat Genet 2009;41:527-534.   DOI   ScienceOn
56 Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 2007;447:661-678.   DOI   ScienceOn
57 Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, Hankinson SE, et al. A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet 2007;39:870-874.   DOI   ScienceOn
58 Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 1997;25:3389-3402.   DOI   ScienceOn
59 Fu Y, Jarboe LR, Dickerson JA. Reconstructing genome-wide regulatory network of E. coli using transcriptome data and predicted transcription factor activities. BMC Bioinformatics 2011;12:233.   DOI
60 Kolaczyk ED. Statistical Analysis of Network Data: Methods and Models. New York: Springer, 2009.
61 Andrade M, Casari G, de Daruvar A, Sander C, Schneider R, Tamames J, et al. Sequence analysis of the Methanococcus jannaschii genome and the prediction of protein function. Comput Appl Biosci 1997;13:481-483.
62 Fetrow JS, Skolnick J. Method for prediction of protein function from sequence using the sequence-to-structure-tofunction paradigm with application to glutaredoxins/thioredoxins and T1 ribonucleases. J Mol Biol 1998;281:949-968.   DOI   ScienceOn
63 Pearson WR, Lipman DJ. Improved tools for biological sequence comparison. Proc Natl Acad Sci U S A 1988;85: 2444-2448.   DOI   ScienceOn
64 Murali TM, Wu CJ, Kasif S. The art of gene function prediction. Nat Biotechnol 2006;24:1474-1475.   DOI   ScienceOn
65 Costanzo MC, Hogan JD, Cusick ME, Davis BP, Fancher AM, Hodges PE, et al. The yeast proteome database (YPD) and Caenorhabditis elegans proteome database (WormPD): comprehensive resources for the organization and comparison of model organism protein information. Nucleic Acids Res 2000;28:73-76.   DOI
66 Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000;403:623-627.   DOI   ScienceOn
67 d'Aspremont A, Banerjee O, El Ghaoui L. First-order methods for sparse covariance selection. SIAM J Matrix Anal Appl 2008;30:56-66.   DOI   ScienceOn
68 Fan J, Feng Y, Wu Y. Network exploration via the adaptive Lasso and Scad penalties. Ann Appl Stat 2009;3:521-541.   DOI
69 Radivojac P, Clark WT, Oron TR, Schnoes AM, Wittkop T, Sokolov A, et al. A large-scale evaluation of computational protein function prediction. Nat Methods 2013;10:221-227.   DOI   ScienceOn
70 Wu J, Vallenius T, Ovaska K, Westermarck J, Mäkelä TP, Hautaniemi S. Integrated network analysis platform for protein- protein interactions. Nat Methods 2009;6:75-77.   DOI   ScienceOn
71 Köhler S, Bauer S, Horn D, Robinson PN. Walking the interactome for prioritization of candidate disease genes. Am J Hum Genet 2008;82:949-958.   DOI   ScienceOn
72 Wu X, Jiang R, Zhang MQ, Li S. Network-based global inference of human disease genes. Mol Syst Biol 2008;4:189.
73 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:e1000641.   DOI   ScienceOn
74 Hwang T, Kuang R. A heterogeneous label propagation algorithm for disease gene discovery. In: Proceeding of the SIAM International Conference on Data Mining, 2010 Apr 29-May 1, Columbus, OH. pp. 583-594.
75 Zhou D, Bousquet O, Lal TN, Weston J, Schölkopf B. Learning with local and global consistency. Adv Neural Inf Process Syst 2004;16:321-328.
76 McKusick VA, Antonarakis SE. Mendelian Inheritance in Man: A Catalog of Human Genes and Genetic Disorders. Baltimore: Johns Hopkins University Press, 1998.
77 Bader GD, Betel D, Hogue CW. BIND: the Biomolecular Interaction Network Database. Nucleic Acids Res 2003;31: 248-250.   DOI   ScienceOn
78 Brown KR, Jurisica I. Online predicted human interaction database. Bioinformatics 2005;21:2076-2082.   DOI   ScienceOn
79 Rebhan M, Chalifa-Caspi V, Prilusky J, Lancet D. GeneCards: integrating information about genes, proteins and diseases. Trends Genet 1997;13:163.   DOI   ScienceOn
80 Rothman AJ, Bickel PJ, Levina E, Zhu J. Sparse permutation invariant covariance estimation. Electron J Stat 2008;2: 494-515.   DOI
81 Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 2008;9: 432-441.   DOI   ScienceOn
82 Schäfer J, Strimmer K. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 2005;21:754-764.   DOI   ScienceOn
83 Cai T, Liu W, Luo X. A constrained l1 minimization approach to sparse precision matrix estimation. J Am Stat Assoc 2011; 106:594-607.   DOI   ScienceOn
84 Myllymäki P, Silander T, Tirri H, Uronen P. B-course: a web-based tool for Bayesian and causal data analysis. Int J Artif Intell Tools 2002;11:369-387.   DOI   ScienceOn
85 Murphy KP. The bayes net toolbox for Matlab. Comput Sci Stat 2001;33:1024-1034.
86 Werhli AV, Grzegorczyk M, Husmeier D. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks. Bioinformatics 2006;22:2523-2531.   DOI   ScienceOn
87 Carter SL, Brechbühler CM, Griffin M, Bond AT. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 2004;20: 2242-2250.   DOI   ScienceOn
88 Zhang B, Horvath S. A general framework for weighted gene co-expression network analysis. Stat Appl Genet Mol Biol 2005;4:Article17.
89 Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008;9:559.   DOI   ScienceOn
90 Butte AJ, Kohane IS. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 2000:418-429.
91 Altman RB, Bergman CM, Blake J, Blaschke C, Cohen A, Gannon F, et al. Text mining for biology: The way forward: opinions from leading scientists. Genome Biol 2008;9 Suppl 2:S7.
92 ENCODE Project Consortium. The ENCODE (ENCyclopedia Of DNA Elements) Project. Science 2004;306:636-640.   DOI   ScienceOn
93 Fan Y, Tang CY. Tuning parameter selection in high dimensional penalized likelihood. J R Stat Soc Series B Stat Methodol 2013;75:531-552.   DOI   ScienceOn
94 Lee I, Blom UM, Wang PI, Shim JE, Marcotte EM. Prioritizing candidate disease genes by network-based boosting of genome- wide association data. Genome Res 2011;21:1109- 1121.   DOI   ScienceOn
95 Akula N, Baranova A, Seto D, Solka J, Nalls MA, Singleton A, et al. A network-based approach to prioritize results from genome- wide association studies. PLoS One 2011;6:e24220.   DOI   ScienceOn
96 Krallinger M, Leitner F, Rodriguez-Penagos C, Valencia A. Overview of the protein-protein interaction annotation extraction task of BioCreative II. Genome Biol 2008;9 Suppl 2:S4.
97 Krallinger M, Valencia A. Text-mining and information- retrieval services for molecular biology. Genome Biol 2005;6:
98 Cheng C, Yan KK, Hwang W, Qian J, Bhardwaj N, Rozowsky J, et al. Construction and analysis of an integrated regulatory network derived from high-throughput sequencing data. PLoS Comput Biol 2011;7:e1002190.   DOI
99 Gerstein MB, Kundaje A, Hariharan M, Landt SG, Yan KK, Cheng C, et al. Architecture of the human regulatory network derived from ENCODE data. Nature 2012;489:91-100.   DOI   ScienceOn
100 Washington NL, Stinson EO, Perry MD, Ruzanov P, Contrino S, Smith R, et al. The modENCODE Data Coordination Center: lessons in harvesting comprehensive experimental details. Database (Oxford) 2011;2011:bar023.
101 Allen JD, Xie Y, Chen M, Girard L, Xiao G. Comparing statistical methods for constructing large scale gene networks. PLoS One 2012;7:e29348.   DOI   ScienceOn
102 Lauritzen SL. Graphical Models. Oxford: Clarendon Press, 1996.
103 Meinshausen N, Buhlmann P. High-dimensional graphs and variable selection with the Lasso. Ann Stat 2006;34:1436- 1462.   DOI   ScienceOn
104 Peng J, Wang P, Zhou N, Zhu J. Partial correlation estimation by joint sparse regression models. J Am Stat Assoc 2009;104:735-746.   DOI   ScienceOn
105 Yuan M, Lin Y. Model selection and estimation in the Gaussian graphical model. Biometrika 2007;94:19-35.   DOI   ScienceOn
106 Banerjee O, El Ghaoui L, d'Aspremont A. Model selection through sparse maximum likelihood estimation for multivariate gaussian or binary data. J Mach Learn Res 2008; 9:485-516.
107 Mewes HW, Frishman D, Gruber C, Geier B, Haase D, Kaps A, et al. MIPS: a database for genomes and protein sequences. Nucleic Acids Res 2000;28:37-40.   DOI
108 Ansorge WJ. Next-generation DNA sequencing techniques. N Biotechnol 2009;25:195-203.   DOI   ScienceOn
109 Fields S, Sternglanz R. The two-hybrid system: an assay for protein-protein interactions. Trends Genet 1994;10:286-292.   DOI   ScienceOn
110 Murali T, Pacifico S, Yu J, Guest S, Roberts GG 3rd, Finley RL Jr. DroID 2011: a comprehensive, integrated resource for protein, transcription factor, RNA and gene interactions for Drosophila. Nucleic Acids Res 2011;39:D736-D743.   DOI
111 Prasad TS, Kandasamy K, Pandey A. Human Protein Reference Database and Human Proteinpedia as discovery tools for systems biology. Methods Mol Biol 2009;577:67-79.   DOI   ScienceOn
112 Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M. BioGRID: a general repository for interaction datasets. Nucleic Acids Res 2006;34:D535-D539.   DOI   ScienceOn
113 Breitkreutz BJ, Stark C, Tyers M. The GRID: the general repository for interaction datasets. Genome Biol 2003;4:R23.   DOI
114 Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D. The Database of Interacting Proteins: 2004 update. Nucleic Acids Res 2004;32:D449-D451.   DOI   ScienceOn
115 Licata L, Briganti L, Peluso D, Perfetto L, Iannuccelli M, Galeota E, et al. MINT, the molecular interaction database: 2012 update. Nucleic Acids Res 2012;40:D857-D861.   DOI
116 Kerrien S, Aranda B, Breuza L, Bridge A, Broackes-Carter F, Chen C, et al. The IntAct molecular interaction database in 2012. Nucleic Acids Res 2012;40:D841-D846.   DOI
117 Jiang C, Xuan Z, Zhao F, Zhang MQ. TRED: a transcriptional regulatory element database, new entries and other development. Nucleic Acids Res 2007;35:D137-D140.   DOI   ScienceOn
118 Zhang X, Liu K, Liu ZP, Duval B, Richer JM, Zhao XM, et al. NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics 2013;29:106-113.   DOI   ScienceOn
119 Wang T, Zhu L. Consistent tuning parameter selection in high dimensional sparse linear regression. J Multivar Anal 2011;102:1141-1151.   DOI   ScienceOn
120 Deng M, Zhang K, Mehta S, Chen T, Sun F. Prediction of protein function using protein-protein interaction data. J Comput Biol 2003;10:947-960.   DOI   ScienceOn
121 Ananiadou S, Pyysalo S, Tsujii J, Kell DB. Event extraction for systems biology by text mining the literature. Trends Biotechnol 2010;28:381-390.   DOI   ScienceOn
122 Barabasi AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet 2004;5: 101-113.   DOI   ScienceOn
123 Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013;41:D808-D815.   DOI
124 Amundadottir L, Kraft P, Stolzenberg-Solomon RZ, Fuchs CS, Petersen GM, Arslan AA, et al. Genome-wide association study identifies variants in the ABO locus associated with susceptibility to pancreatic cancer. Nat Genet 2009;41:986-990.   DOI   ScienceOn
125 Ulfarsson MO, Solo V. Tuning parameter selection for underdetermined reduced-rank regression. IEEE Signal Process Lett 2013;20:881-884.   DOI   ScienceOn
126 Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A. Reverse engineering of regulatory networks in human B cells. Nat Genet 2005;37:382-390.   DOI   ScienceOn