Inference of gene pathways using Gaussian mixture models

Proceedings - 2007 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2007

Volumn , Issue , 2007, Pages 362-367

  Ko, Younhee ^a   Zhai, Chengxiang ^a   Rodriguez Zas, Sandra L ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

Author keywords

Bayesian information criterion; Bayesian networks; Microarray; Mixture model

Indexed keywords

BAYESIAN NETWORKS; BIOACTIVITY; BIOCOMMUNICATIONS; BIOINFORMATICS; COMMUNICATION CHANNELS (INFORMATION THEORY); DISTRIBUTED PARAMETER NETWORKS; ELECTRIC NETWORK TOPOLOGY; ESTIMATION; GENE EXPRESSION; INFERENCE ENGINES; INFORMATION SCIENCE; INTELLIGENT NETWORKS; MIXTURES; OBJECT RECOGNITION; PARAMETER ESTIMATION; SPEECH ANALYSIS; TOPOLOGY; TRELLIS CODES;

BAYESIAN; BAYESIAN INFORMATION CRITERION; BIOLOGICAL PROCESSING; CIRCADIAN RHYTHMS; CONTINUOUS DATA; E-M ALGORITHMS; GAUSSIAN MIXTURE MODELING; GENE EXPRESSION DATA; GENE-GENE INTERACTIONS; INTERNATIONAL CONFERENCES; MICROARRAY; MIXTURE MODEL; NETWORK ALGORITHMS; OPTIMAL NETWORK TOPOLOGY; OPTIMAL NUMBER;

MINE FLOODING;

EID: 39449124154     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/BIBM.2007.59     Document Type: Conference Paper

References (19)

1. th European Conference on Artificial Intelligence, 2004, pp. 263-267.
2. Bernard, A. and Hartemink, A., " Informative structure priors: joint learning of dynamic regulatory networks from multiple types of data ", PSB, 2006, pp. 459-470.
3. Chen, X., Anantha, G., and Wang, X., " An effective structure learning method for constructing gene networks ", Bioinformatics, 22, 2006, pp. 1367-1374.
4. Davies, S., Moore, A. , " Mix-nets: Factored mixtures of Gaussians in Bayesian networks with mixed continuous and discrete variables ", 2000, CMU-CS-00-119.
5. Friedman, N., Iftach N., and Peer, D., " Learning Bayesian network structure from massive datasets: The Sparse Candidate algorithm ", Uncertainty in Artificial Intelligence (UAI)s, 1999, pp. 206-215.
6. Hartemink, A. et al., " Combining location and expression data for principled discovery of genetic regulatory networks ", PSB, 2002, pp. 437-449.
7. Heckerman, D., " A tutorial n learning Bayesian networks ", Technical Report MSR-TR-95-06 Microsoft Research., 1996.
8. Husmeier, D., " Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks ", Bioinformatics, 2003, 19, pp. 2271-2282.
9. Otta, S. et al., " Finding optimal models for small gene networks ", PSB, 2004, pp. 555-567.
10. Pena, J. et al., "Growing Bayesian network models of gene networks from seed genes ", Bioinformatics, 2005, 21, pp. i224-i229.
11. Perrin, B.E, et al., Gene networks inference using dynamic Bayesian networks ", Bioinformatics, 2003, 19,pp. i138-i148.
12. Pournara, I. and Wernisch, L., " Reconstruction of gene networks using Bayesian learning and manipulation experiments ", Bioinformatics, 2004, 20,pp. 2934-2942.
13. Smith, V.A. et al., " Evaluating functional network inference using simulations of complex biological systems Bioinformatics, 2002, 18, pp, s216-s224.
14. Spellman, P.T, et al., " Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization", Mol. Biol. Cell, 1999, 9, pp. 3273-3297.
15. Vlassis, N. and Likas, A., A greedy EM algorithm for Gaussian mixture learning ", Neutral Processing Letters, 2002, pp. 263-267.
16. Yu, J. et al., " Advances to Bayesian network inference for generating causal networks from observational biological data ", Bioinformatics, 2004, 20, pp. 3594-3603.
17. Zhou, X. et al.., " A new dynamic Bayesian network(DBN) approach for identifying gene regulatory networks from time course microarray data ", Bioinformatics, 2001, 21, pp. 71-79.
18. Rodriguez-Zas SL, Southey BR, Whitfield CW, Robinson GE.," Development and implementation of longitudinal linear and nonlinear models to describe changes in gene expression across ages", BMC Genomics, 2006, Sep, 13, 7:233.
19. "KEGG Kyoto Encyclopedia of Genes and Genomes", http://www.genome.jp/kegg/.