Reconstruct modular phenotype-specific gene networks by knowledge-driven matrix factorization. Export

@article{citeulike:5400242, abstract = {MOTIVATION: Reconstructing gene networks from microarray data has provided mechanistic information on cellular processes. A popular structure learning method, Bayesian network inference, has been used to determine network topology despite its shortcomings, i.e. the high-computational cost when analyzing a large number of genes and the inefficiency in exploiting prior knowledge, such as the co-regulation information of the genes. To address these limitations, we are introducing an alternative method, knowledge-driven matrix factorization (KMF) framework, to reconstruct phenotype-specific modular gene networks. RESULTS: Considering the reconstruction of gene network as a matrix factorization problem, we first use the gene expression data to estimate a correlation matrix, and then factorize the correlation matrix to recover the gene modules and the interactions between them. Prior knowledge from Gene Ontology is integrated into the matrix factorization. We applied this KMF algorithm to hepatocellular carcinoma (HepG2) cells treated with free fatty acids (FFAs). By comparing the module networks for the different conditions, we identified the specific modules that are involved in conferring the cytotoxic phenotype induced by palmitate. Further analysis of the gene modules of the different conditions suggested individual genes that play important roles in palmitate-induced cytotoxicity. In summary, KMF can efficiently integrate gene expression data with prior knowledge, thereby providing a powerful method of reconstructing phenotype-specific gene networks and valuable insights into the mechanisms that govern the phenotype.}, author = {Yang, Xuerui and Zhou, Yang and Jin, Rong and Chan, Christina}, citeulike-article-id = {5400242}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btp376}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/25/17/2236}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/19542155}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=19542155}, comment = {"A popular structure learning method, Bayesian network inference, has been used to determine network topology despite its shortcomings, i.e. the high-computational cost when analyzing a large number of genes and the inefficiency in exploiting prior knowledge, such as the co-regulation information of the genes."}, day = {1}, doi = {10.1093/bioinformatics/btp376}, issn = {1367-4811}, journal = {Bioinformatics (Oxford, England)}, keywords = {algorithm\_developers, co-expression, gene\_expression\_data, gene\_networks, microarray\_data, network\_topology, networks, pathways, regulatory\_pathways, systems\_biology}, month = {September}, number = {17}, pages = {2236--2243}, posted-at = {2009-11-10 11:25:34}, priority = {2}, title = {Reconstruct modular phenotype-specific gene networks by knowledge-driven matrix factorization.}, url = {http://dx.doi.org/10.1093/bioinformatics/btp376}, volume = {25}, year = {2009} }

TY - JOUR ID - citeulike:5400242 L3 - citeulike-article-id:5400242 N2 - MOTIVATION: Reconstructing gene networks from microarray data has provided mechanistic information on cellular processes. A popular structure learning method, Bayesian network inference, has been used to determine network topology despite its shortcomings, i.e. the high-computational cost when analyzing a large number of genes and the inefficiency in exploiting prior knowledge, such as the co-regulation information of the genes. To address these limitations, we are introducing an alternative method, knowledge-driven matrix factorization (KMF) framework, to reconstruct phenotype-specific modular gene networks. RESULTS: Considering the reconstruction of gene network as a matrix factorization problem, we first use the gene expression data to estimate a correlation matrix, and then factorize the correlation matrix to recover the gene modules and the interactions between them. Prior knowledge from Gene Ontology is integrated into the matrix factorization. We applied this KMF algorithm to hepatocellular carcinoma (HepG2) cells treated with free fatty acids (FFAs). By comparing the module networks for the different conditions, we identified the specific modules that are involved in conferring the cytotoxic phenotype induced by palmitate. Further analysis of the gene modules of the different conditions suggested individual genes that play important roles in palmitate-induced cytotoxicity. In summary, KMF can efficiently integrate gene expression data with prior knowledge, thereby providing a powerful method of reconstructing phenotype-specific gene networks and valuable insights into the mechanisms that govern the phenotype. IS - 17 JF - Bioinformatics (Oxford, England) SN - 1367-4811 EP - 2243 TI - Reconstruct modular phenotype-specific gene networks by knowledge-driven matrix factorization. VL - 25 SP - 2236 KW - algorithm_developers KW - co-expression KW - gene_expression_data KW - gene_networks KW - microarray_data KW - network_topology KW - networks KW - pathways KW - regulatory_pathways KW - systems_biology N1 - "A popular structure learning method, Bayesian network inference, has been used to determine network topology despite its shortcomings, i.e. the high-computational cost when analyzing a large number of genes and the inefficiency in exploiting prior knowledge, such as the co-regulation information of the genes." AU - Yang, Xuerui AU - Zhou, Yang AU - Jin, Rong AU - Chan, Christina PY - 2009/09/01/ UR - http://dx.doi.org/10.1093/bioinformatics/btp376 ER -