Review on statistical methods for gene network reconstruction using expression data

Journal of Theoretical Biology

Volumn 362, Issue , 2014, Pages 53-61

  Wang, Y X Rachel ^a   Huang, Haiyan ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Bayesian networks; Coexpression networks; Community detection; Dynamic networks; Genomic data integration

Indexed keywords

BAYESIAN ANALYSIS; GENE EXPRESSION; GENETIC ANALYSIS; GEOSTATISTICS; NETWORK ANALYSIS; RECONSTRUCTION;

ARTICLE; BEHAVIOR; DATA PROCESSING; GENE EXPRESSION; GENE INTERACTION; GENE REGULATORY NETWORK; GENETIC ASSOCIATION; INFERENTIAL STATISTICS; STATISTICAL ANALYSIS; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; CLUSTER ANALYSIS; COMPUTER PROGRAM; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENOMICS; HUMAN; METABOLISM; NORMAL DISTRIBUTION; STATISTICAL MODEL;

BIOLOGICAL MARKER;

ALGORITHMS; BAYES THEOREM; BIOMARKERS; CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; GENOMICS; HUMANS; MODELS, STATISTICAL; NORMAL DISTRIBUTION; PATTERN RECOGNITION, AUTOMATED; SOFTWARE;

EID: 84921978377     PISSN: 00225193     EISSN: 10958541     Source Type: Journal    
DOI: 10.1016/j.jtbi.2014.03.040     Document Type: Article

References (148)

1. Airoldi E.M., Blei D.M., Fienberg S.E., Xing E.P. Mixed membership stochastic blockmodels. J. Mach. Learn. Res. 2008, 9:1981-2014.
2. Akutsu, T., Miyano, S., Kuhara, S., 1999. Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. In: Pacific Symposium on Biocomputing. pp. 17-28.
3. Alon U., Barkai N., Notterman D.A., Gish K., Ybarra S., Mack D., Levine A.J. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc. Natl. Acad. Sci. 1999, 96:6745-6750.
4. Aloy P., Russell R.B. InterPreTS. protein interaction prediction through tertiary structure. Bioinformatics 2003, 19:161-162.
5. Amini A.A., Chen A., Bickel P.J., Levina E. Pseudo-likelihood methods for community detection in large sparse networks. Ann. Stat. 2013, 41:2097-2122.
6. Azuaje F. Bioinformatics and Biomarker Discovery. "Omic" Data Analysis for Personalized Medicine 2010, John Wiley & Sons, West Sussex, UK.
7. Bar-Joseph Z., Gerber G.K., Lee T.I., Rinaldi N.J., Yoo J.Y., Robert F., Gordon D.B., Fraenkel E., Jaakkola T.S., Young R.A., Gifford D.K. Computational discovery of gene modules and regulatory networks. Nat. Biotechnol. 2003, 21:1337-1342.
8. Bar-Joseph Z., Gitter A., Simon I. Studying and modelling dynamic biological processes using time-series gene expression data. Nat. Rev. Genetics 2012, 13:552-564.
9. Barabási A.L., Oltvai Z.N. Network biology. understanding the cell's functional organization. Nat. Rev. Genetics 2004, 5:101-113.
10. Barrett T., Troup D.B., Wilhite S.E., Ledoux P., Rudnev D., Evangelista C., Kim I.F., Soboleva A., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Muertter R.N., Edgar R. NCBI GEO. archive for high-throughput functional genomic data. Nucl. Acids Res. 2009, 37:D885-D890.
11. Basso K., Margolin A.A., Stolovitzky G., Klein U., Dalla-Favera R., Califano A. Reverse engineering of regulatory networks in human b cells. Nat. Genetics 2005, 37:382-390.
12. Becker S.A., Palsson B.O. Context-specific metabolic networks are consistent with experiments. PLoS Comput. Biol. 2008, 4:e1000082.
13. Ben-Dor A., Shamir R., Yakhini Z. Clustering gene expression patterns. J. Comput. Biol. 1999, 6:281-297.
14. Bickel D.R. Probabilities of spurious connections in gene networks. application to expression time series. Bioinformatics 2005, 21:1121-1128.
15. Bickel P., Chen A. A nonparametric view of network models and Newman-Girvan and other modularities. Proc. Natl. Acad. Sci. 2009, 106:21068-21073.
16. Bickel P.J., Choi D., Chang X., Zhang H. Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels. Ann. Stat. 2013, 41:1922-1943.
17. Bleakley K., Biau G., Vert J.P. Supervised reconstruction of biological networks with local models. Bioinformatics 2007, 23:i57-i65.
18. Bonneau R., Reiss D.J., Shannon P., Facciotti M., Hood L., Baliga N.S., Thorsson V. The Inferelator. an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 2006, 7:R36.
19. Butte, A.J., Kohane, I.S., 2000. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. in: Pacific Symposium on Biocomputing. pp. 418-429.
20. Cai L., Huang H., Blackshaw S., Liu J.S., Cepko C., Wong W.H. Clustering analysis of SAGE data using a Poisson approach. Genome Biol. 2004, 5:R51.
21. Carter S.L., Brechbühler C.M., Griffin M., Bond A.T. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics 2004, 20:2242-2250.
22. Celisse A., Daudin J.J., Pierre L. Consistency of maximum-likelihood and variational estimators in the stochastic block model. Electron. J. Stat. 2012, 6:1847-1899.
23. Cerulo L., Elkan C., Ceccarelli M. Learning gene regulatory networks from only positive and unlabeled data. BMC Bioinformat. 2010, 11:228.
24. Channarond A., Daudin J.-J., Robin S. Classification and estimation in the stochastic block model based on the empirical degrees. Electron. J. Stat. 2012, 6:2574-2601.
25. Chen K.C., Wang T.Y., Tseng H.H., Huang C.Y., Kao C.Y. A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics 2005, 21:2883-2890.
26. Chen T., Filkov V., Skiena S. Identifying gene regulatory networks from experimental data. Parallel Comput. 2001, 27:141-162.
27. Cheng Y., Church G.M. Biclustering of expression data. Int. Conf. Intell. Syst. Mol. Biol. 2000, 8:93-103.
28. Cooper G.F., Herskovits E. A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 1992, 9:309-347.
29. Daub C.O., Steuer R., Selbig J., Kloska S. Estimating mutual information using B-spline functions - an improved similarity measure for analysing gene expression data. BMC Bioinformat. 2004, 5:118.
30. Daudin J.J., Picard F., Robin S. A mixture model for random graphs. Stat. Comput. 2008, 18:173-183.
31. De La Fuente A., Bing N., Hoeschele I., Mendes P. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 2004, 20:3565-3574.
32. De Smet R., Marchal K. Advantages and limitations of current network inference methods. Nat. Rev. Microbiol. 2010, 8:717-729.
33. Decelle A., Krzakala F., Moore C., Zdeborová L. Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications. Phys. Rev. E 2011, 84:066106.
34. Dembélé D., Kastner P. Fuzzy C-means method for clustering microarray data. Bioinformatics 2003, 19:973-980.
35. Dennis G.J., Sherman B.T., Hosack D.A., Yang J., Gao W., Lane H.C., Lempicki R.A. DAVID. database for annotation, visualization, and integrated discovery. Genome Biol. 2003, 4:P3.
36. D'haeseleer P., Liang S., Somogyi R. Genetic network inference. from co-expression clustering to reverse engineering. Bioinformatics 2000, 16:707-726.
37. di Bernardo D., Thompson M.J., Gardner T.S., Chobot S.E., Eastwood E.L., Wojtovich A.P., Elliott S.J., Schaus S.E., Collins J.J. Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 2005, 23:377-383.
38. Edwards D.I. Introduction to Graphical Modelling 2000, Springer, New York, USA. 2nd ed.
39. Eisen M.B., Spellman P.T., Brown P.O., Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. 1998, 95:14863-14868.
40. Ernst J., Beg Q.K., Kay K.A., Balázsi G., Oltvai Z.N., Bar-Joseph Z. A semi-supervised method for predicting transcription factor-gene interactions in Escherichia coli. PLoS Comput. Biol. 2008, 4:e1000044.
41. Feist A.M., Herrgård M.J., Thiele I., Reed J.L., Palsson B.O. Reconstruction of biochemical networks in microorganisms. Nat. Rev. Microbiol. 2009, 7:129-143.
42. Filkov V., Skiena S., Zhi J. Analysis techniques for microarray time-series data. J. Comput. Biol. 2002, 9:317-330.
43. Fishkind D., Sussman D., Tang M., Vogelstein J., Priebe C. Consistent adjacency-spectral partitioning for the stochastic block model when the model parameters are unknown. SIAM J. Matrix Anal. Appl. 2013, 34:23-29.
44. Friedman J., Hastie T., Tibshirani R. Sparse inverse covariance estimation with the graphical Lasso. Biostatistics 2007, 9:432-441.
45. Friedman N., Linial M., Nachman I., Pe'er D. Using Bayesian networks to analyze expression data. J. Comput. Biol. 2000, 7:601-620.
46. Fu L., Medico E. FLAME, a novel fuzzy clustering method for the analysis of DNA microarray data. BMC Bioinformat. 2007, 8:3.
47. Gardner T.S., di Bernardo D., Lorenz D., Collins J.J. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 2003, 301:102-105.
48. Gesù V.D., Giancarlo R., Bosco G.L., Raimondi A., Scaturro D. GenClust. a genetic algorithm for clustering gene expression data. BMC Bioinformat. 2005, 6:289.
49. Gillis J., Pavlidis P. "Guilt by association" is the exception rather than the rule in gene networks. PLoS Comput. Biol. 2012, 8:e1002444.
50. Guimerà R., Amaral L.A.N. Functional cartography of complex metabolic networks. Nature 2005, 433:895-900.
51. Haggart C.R., Bartell J.A., Saucerman J.J., Papin J.A. Whole-genome metabolic network reconstruction and constraint-based modeling. Meth. Enzymol. 2011, 500:411-433.
52. Hartemink, A.J., Gifford, D.K., Jaakkola, T.S., Young, R.A., 2001. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. in: Pacific Symposium on Biocomputing. pp. 422-433.
53. Heckerman, D., 1996. A Tutorial on Learning with Bayesian Networks. Technical Report, Learning in Graphical Models.
54. Hofman J.M., Wiggins C.H. A Bayesian approach to network modularity. Phys. Rev. Lett. 2008, 100:258701.
55. Holland P.W., Laskey K.B., Leinhardt S. Stochastic blockmodels. first steps. Soc. Netw. 1983, 5:109-137.
56. Horvath S., Dong J. Geometric interpretation of gene coexpression network analysis. PLoS Comput. Biol. 2008, 4:e1000117.
57. Ideker, T.E., Thorsson, V., Karp, R.M., 2000. Discovery of regulatory interactions through perturbation: inference and experimental design. In: Pacific Symposium on Biocomputing. pp. 305-316.
58. Jain A.K., Murty M.N., Flynn P.J. Data clustering. a review. ACM Comput. Surv. 1999, 31:264-323.
59. Jansen R., Yu H., Greenbaum D., Kluger Y., Krogan N.J., Chung S., Emili A., Snyder M., Greenblatt J.F., Gerstein M. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 2003, 302:449-453.
60. Jensen, L.J., Kuhn, M., Stark, M., Chaffron, S., Creevey, C., Muller, J., Doerks, T., Julien, P., Roth, A., Simonovic, M., Bork, P., von Mering, C., 2009. STRING 8-a global view on proteins and their functional interactions in 630 organisms. Nucl. Acids Res. 37, D412-D416.
61. Jiang D., Tang C., Zhang A. Cluster analysis for gene expression data. a survey. IEEE Trans. Knowl. Data Eng. 2004, 16:1370-1386.
62. Karrer B., Newman M.E.J. Stochastic blockmodels and community structure in networks. Phys. Rev. E 2011, 83:016107.
63. Kauffman S.A. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theoret. Biol. 1969, 22:437-467.
64. Kaufman L., Rousseeuw P.J. Finding Groups in Data. an Introduction to Cluster Analysis 2005, John Wiley and Sons, New York.
65. Kerr G., Ruskin H.J., Crane M., Doolan P. Techniques for clustering gene expression data. Comput. Biol. Med. 2008, 38:283-293.
66. Kim K., Jiang K., Teng S.M., Feldman L.J., Huang H. Using biologically interrelated experiments to identify pathway genes in Arabidopsis. Bioinformatics 2012, 28:815-822.
67. Kim K., Zhang S., Jiang K., Cai L., Lee I.B., Feldman L.J., Huang H. Measuring similarities between gene expression profiles through new data transformations. BMC Bioinformat. 2007, 8:29.
68. Kim S., Imoto S., Miyano S. Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data. Biosystems 2004, 75:57-65.
69. Kimura S., Ide K., Kashihara A., Kano M., Hatakeyama M., Masui R., Nakagawa N., Yokoyama S., Kuramitsu S., Konagaya A. Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics 2005, 21:1154-1163.
70. Kinney J.B., Atwal G.S. Equitability, mutual information, and the maximal information coefficient. Proc. Natl. Acad. Sci. 2014, 111:3354-3359.
71. Kohl M., Wiese S., Warscheid B. Cytoscape. software for visualization and analysis of biological networks. Meth. Mol. Biol. 2011, 696:291-303.
72. Kumari S., Nie J., Chen H.S., Ma H., Stewart R., Li X., Lu M.Z., Taylor W.M., Wei H. Evaluation of gene association methods for coexpression network construction and biological knowledge discovery. PLoS ONE 2012, 7:e50411.
73. Langfelder P., Horvath S. WGCNA. an R package for weighted correlation network analysis. BMC Bioinformat. 2008, 9:559.
74. Langfelder P., Zhang B., Horvath S. Defining clusters from a hierarchical cluster tree. The Dynamic Tree Cut package for R. Bioinformatics 2008, 24:719-720.
75. Latouche P., Birmelé E., Ambroise C. Variational Bayesian inference and complexity control for stochastic block models. Stat. Modell. 2012, 12:93-115.
76. Lemmens K., Dhollander T., De Bie T., Monsieurs P., Engelen K., Smets B., Winderickx J., De Moor B., Marchal K. Inferring transcriptional modules from chip-chip, motif and microarray data. Genome Biol. 2006, 7:R37.
77. Li H., Gui J. Gradient directed regularization for sparse Gaussian concentration graphs, with applications to inference of genetic networks. Biostatistics 2006, 7:302-317.
78. Li K.-C. Genome-wide coexpression dynamics. theory and application. Proc. Natl. Acad. Sci. 2002, 99:16875-16880.
79. Li K.-C., Palotie A., Yuan S., Bronnikov D., Chen D., Wei X., Choi O.-W., Saarela J., Peltonen L. Finding disease candidate genes by liquid association. Genome Biol. 2007, 8:R205.
80. Liang, S., Fuhrman, S., Somogyi, R., 1998. Reveal, a general reverse engineering algorithm for inference of genetic network architectures. In: Pacific Symposium on Biocomputing. pp. 18-29.
81. Lim J., Hao T., Shaw C., Patel A.J., Szabó G., Rual J.F., Fisk C.J., Li N., Smolyar A., Hill D.E., Barabási A.L., Vidal M., Zoghbi H.Y. A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration. Cell 2006, 125:801-814.
82. Ma P., Castillo-Davis C.I., Zhong W., Liu J.S. A data-driven clustering method for time course gene expression data. Nucl. Acids Res. 2006, 34:1261-1269.
83. Madeira S.C., Oliveira A.L. Biclustering algorithms for biological data analysis. a survey. IEEE/ACM Trans. Comput. Biol. Bioinform. 2004, 1:24-45.
84. Maetschke S.R., Madhamshettiwar P.B., Davis M.J., Ragan M.A. Supervised, semi-supervised and unsupervised inference of gene regulatory networks. Brief. Bioinform. 2014, 15:195-211.
85. Magwene P., Kim J. Estimating genomic coexpression networks using first-order conditional independence. Genome Biol. 2004, 5:R100.
86. Margolin A.A., Nemenman I., Basso K., Wiggins C., Stolovitzky G., Dalla-Favera R., Califano A. ARACNE. an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 2006, 7:S7.
87. Markowetz F., Kostka D., Troyanskaya O.G., Spang R. Nested effects models for high-dimensional phenotyping screens. Bioinformatics 2007, 23:i305-i312.
88. Mazur J., Ritter D., Reinelt G., Kaderali L. Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling. BMC Bioinform. 2009, 10:448.
89. Meinshausen N., Bühlmann P. High-dimensional graphs and variable selection with the Lasso. Ann. Stat. 2006, 34:1049-1579.
90. Mordelet F., Vert J.P. SIRENE. supervised inference of regulatory networks. Bioinformatics 2008, 24:i76-i82.
91. Muro S., Takemasa I., Oba S., Matoba R., Ueno N., Maruyama C., Yamashita R., Sekimoto M., Yamamoto H., Nakamori S., Monden M., Ishii S., Kato K. Identification of expressed genes linked to malignancy of human colorectal carcinoma by parametric clustering of quantitative expression data. Genome Biol. 2003, 4:R21.
92. Murphy, K., 2002. Dynamic Bayesian Networks: Representation, Inference and Learning (Ph.D. thesis). UC Berkeley, Computer Science Division.
93. Needham C.J., Bradford J.R., Bulpitt A.J., Westhead D.R. A primer on learning in Bayesian networks for computational biology. PLoS Comput. Biol. 2007, 3:e129.
94. Newman M.E.J., Girvan M. Finding and evaluating community structure in networks. Phys. Rev. E 2004, 69:026113.
95. Nowicki K., Snijders T.A.B. Estimation and prediction for stochastic blockstructures. J. Am. Stat. Assoc. 2001, 96:1077-1087.
96. Okuda S., Yamada T., Hamajima M., Itoh M., Katayama T., Bork P., Goto S., Kanehisa M. KEGG Atlas mapping for global analysis of metabolic pathways. Nucl. Acids Res. 2008, 36:W423-W426.
97. Orth J.D., Thiele I., Palsson B.O. What is flux balance analysis?. Nat. Biotechnol. 2010, 28:245-248.
98. Pearl, J., Verma, T., 1991. A theory of inferred causation. in: (KR 1991), pp. 441-452.
99. Pe'er, D., Regev, A., Elidan, G., N., F., 2001. Inferring subnetworks from perturbed expression profiles. Bioinformatics 17, S215-S224.
100. Pellegrini M., Marcotte E.M., Thompson M.J., Eisenberg D., Yeates T.O. Assigning protein functions by comparative genome analysis. protein phylogenetic profiles. Proc. Natl. Acad. Sci. 1999, 96:4285-4288.
101. Peng J., Wang P., Zhou N., Zhu J. Partial correlation estimation by joint sparse regression models. J. Am. Stat. Assoc. 2009, 104:736-746.
102. Penrose R. A generalized inverse for matrices. Math. Proc. Cambridge Philos. Soc. 1955, 51:406-413.
103. Price N.D., Reed J.L., Palsson B.O. Genome-scale models of microbial cells. evaluating the consequences of constraints. Nat. Rev. Microbiol. 2004, 2:886-897.
104. Ramesh, A., Trevino, R., VON Hoff, D.D., Kim, S., 2010. Clustering context-specific gene regulatory networks. In: Pacific Symposium on Biocomputing. pp. 444-455.
105. Rényi A. On measure of dependence. Acta Math. Academiae Scientiarum Hungarica 1959, 10:441-451.
106. Reshef D.N., Reshef Y.A., Finucane H.K., Grossman S.R., McVean G., Turnbaugh P.J., Lander E.S., Mitzenmacher M., Sabeti P.C. Detecting novel associations in large data sets. Science 2011, 334:1518-1524.
107. Rohe K., Chatterjee S., Yu B. Spectral clustering and the high-dimensional stochastic block model. Ann. Stat. 2011, 39:1878-1915.
108. Sabatti C., James G.M. Bayesian sparse hidden components analysis for transcription regulation networks. Bioinformatics 2006, 22:739-746.
109. Savageau M.A. Biochemical systems theory. operational differences among variant representations and their significance. J. Theor. Biol. 1991, 151:509-530.
110. Schäfer J., Strimmer K. An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 2005, 21(6):754-764.
111. Sharan R., Maron-Katz A., Shamir R. CLICK and EXPANDER. a system for clustering and visualizing gene expression data. Bioinformatics 2003, 19:1787-1799.
112. Shlomi T., Cabili M.N., Herrgård M.J., Palsson B.O., Ruppin E. Network-based prediction of human tissue-specific metabolism. Nat. Biotechnol. 2008, 26:1003-1010.
113. Shojaie, A., Jauhiainen, A., Kallitsis, M., Michailidis, G., 2013. Inferring regulatory networks by combining perturbation screens and steady state gene expression profiles. http://www.arXiv:1312.0335.
114. Snijders T.A.B., Nowicki K. Estimation and prediction for stochastic blockmodels for graphs with latent block structure. J. Classif. 1997, 14:75-100.
115. Soler-López M., Zanzoni A., Lluís R., Stelzl U., Aloy P. Interactome mapping suggests new mechanistic details underlying Alzheimer's disease. Genome Res. 2011, 21:364-376.
116. Spellman P.T., Sherlock G., Zhang M.Q., Iyer V.R., Anders K., Eisen M.B., Brown P.O., Botstein D., Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 1998, 9:3273-3297.
117. Spieth, C., Streichert, F., Speer, N., Zell, A., 2004. A memetic inference method for gene regulatory networks based on S-Systems. In: Proceedings of the IEEE Congress on Evolutionary Computation. pp. 152-157.
118. Steuer, R., Kurths, J., Daub, C.O., Weise, J., Selbig, J., 2002. The mutual information: detecting and evaluating dependencies between variables. Bioinformatics 18, S231-S240.
119. Stuart J.M., Segal E., Koller D., Kim S.K. A gene-coexpression network for global discovery of conserved genetic modules. Science 2003, 302:249-255.
120. Tamayo P., Slonim D., Mesirov J., Zhu Q., Kitareewan S., Dmitrovsky E., Lander E.S., Golub T.R. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci. 1999, 96:2907-2912.
121. Tanay A., Sharan R., Kupiec M., Shamir R. Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc. Natl. Acad. Sci. 2004, 101:2981-2986.
122. Tavazoie S., Hughes J.D., Campbell M.J., Cho R.J., Church G.M. Systematic determination of genetic network architecture. Nat. Genetics 1999, 22:281-285.
123. Teng S.L., Huang H. A statistical framework to infer functional gene relationships from biologically interrelated microarray experiments. J. Am. Stat. Assoc. 2009, 104(486):465-473.
124. Teschendorff A.E., Wang Y., Barbosa-Morais N.L., Brenton J.D., Caldas C. A variational Bayesian mixture modelling framework for cluster analysis of gene-expression data. Bioinformatics 2005, 21:3025-3033.
125. Theodoridis S., Koutroumbas K. Pattern Recognition 2005, Academic Press, San Diego, USA. 4th edition.
126. Tresch A., Markowetz F. Structure learning in nested effects models. Stat. Appl. Genetics Mol. Biol. 2008, 7:9.
127. Wang Y., Joshi T., Zhang X.S., Xu D., Chen L. Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics 2006, 22:2413-2420.
128. Wang Y., Zhang X.-S., Chen L. A network biology study on circadian rhythm by integrating various omics data. OMICS: J. Integr. Biol. 2009, 13:313-324.
129. Wang Y., Zhang X.-S., Xia Y. Predicting eukaryotic transcriptional cooperativity by Bayesian network integration of genome-wide data. Nucl. Acids Res. 2009, 37:5943-5958.
130. Wang, Y.X.R., Jiang, K., Feldman, L.J., Bickel, P.J., Huang, H., 2014. Inferring gene association networks using sparse canonical correlation analysis. http://www.arXiv:1401.6504.
131. Wen X., Fuhrman S., Michaels G.S., Carr D.B., Smith S., Barker J.L., Somogyi R. Large-scale temporal gene expression mapping of central nervous system development. Proc. Natl. Acad. Sci. 1998, 95:334-339.
132. Wille A., Bühlmann P. Low-order conditional independence graphs for inferring genetic networks. Stat. Appl. Genetics Mol. Biol. 2006, 5:1.
133. Wille, A., Zimmermann, P., Vranova, E., Furholz, A., Laule, O., Bleuler, S., Hennig, L., Prelic, A., von Rohr, P., Thiele, L., Zitzler, E., Gruissem, W., Bühlmann, P., 2004. Sparse graphical Gaussian modeling of the isoprenoid gene network in arabidopsis thaliana. Genome Biol. 5, 1-13.
134. Wingender E., Chen X., Fricke E., Geffers R., Hehl R., Liebich I., Krull M., Matys V., Michael H., Ohnhäuser R., Prüss M., Schacherer F., Thiele S., Urbach S. The TRANSFAC system on gene expression regulation. Nucl. Acids Res. 2001, 29:281-283.
135. Wolfe C.J., Kohane I.S., Butte A.J. Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks. BMC Bioinformat. 2005, 6:227.
136. Xiong Q., Ancona N., Hauser E.R., Mukherjee S., Furey T.S. Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets. Genome Res. 2012, 22:386-397.
137. Yeung K.Y., Fraley C., Murua A., Raftery A.E., Ruzzo W.L. Model-based clustering and data transformations for gene expression data. Bioinformatics 2001, 17:977-987.
138. Yeung M.K., Tegnér J., Collins J.J. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. 2002, 99:6163-6168.
139. Yip K.Y., Gerstein M. Training set expansion. an approach to improving the reconstruction of biological networks from limited and uneven reliable interactions. Bioinformatics 2009, 25:243-250.
140. Yizhak K., Benyamini T., Liebermeister W., Ruppin E., Shlomi T. Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model. Bioinformatics 2010, 26:i255-i260.
141. Yoo, C., Thorsson, V., Cooper, G., 2002. Discovery of causal relationships in a gene-regulation pathway from a mixture of experimental and observational DNA microarray data. In: Pacific Symposium on Biocomputing. pp. 498-509.
142. Yu, J., Smith, V.A., Wang, P.P., Hartemink, A.J., Jarvis, E.D., 2002. Using Bayesian network inference algorithms to recover molecular genetic regulatory networks. In: International Conference on Systems Biology.
143. Yu J., Smith V.A., Wang P.P., Hartemink A.J., Jarvis E.D. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 2004, 20:3594-3603.
144. Yuan M., Lin Y. Model selection and estimation in the Gaussian graphical model. Biometrika 2007, 94:19-35.
145. Zhang B., Horvath S. A general framework for weighted gene co-expression network analysis. Stat. Appl. Genetics Mol. Biol. 2005, 4:17.
146. Zhou S., Rütimann P., Xu M., Bühlmann P. High-dimensional covariance estimation based on Gaussian graphical models. J. Mach. Learn. Res. 2011, 12:2975-3026.
147. Zhu J., Chen Y., Leonardson A.S., Wang K., Lamb J.R., Emilsson V., Schadt E.E. Characterizing dynamic changes in the human blood transcriptional network. PLoS Comput. Biol. 2010, 6:e1000671.
148. Zou M., Conzen S.D. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 2005, 21:71-79.