Modularized learning of genetic interaction networks from biological annotations and mRNA expression data Export

@article{citeulike:213058, abstract = {Motivation: Inferring the genetic interaction mechanism using Bayesian networks has recently drawn increasing attention due to its well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. Results: We propose a novel method to infer genetic networks by alleviating the shortage of available mRNA expression data with prior knowledge. We call the proposed method modularized network learning' (MONET). Firstly, the proposed method divides a whole gene set to overlapped modules considering biological annotations and expression data together. Secondly, it infers a Bayesian network for each module, and integrates the learned subnetworks to a global network. An algorithm that measures a similarity between genes based on hierarchy, specificity and multiplicity of biological annotations is presented. The proposed method draws a global picture of inter-module relationships as well as a detailed look of intra-module interactions. We applied the proposed method to analyze Saccharomyces cerevisiae stress data, and found several hypotheses to suggest putative functions of unclassified genes. We also compared the proposed method with a whole-set-based approach and two expression-based clustering approaches. Availability: JAVA programs for the MONET framework are available from the corresponding author upon request. Web supplementary data is accessible at http://biosoft.kaist.ac.kr/\~{}dhlee/monet/index.html Contact: doheon@kaist.ac.kr 10.1093/bioinformatics/bti406}, author = {Lee, Phil H. and Lee, Doheon}, citeulike-article-id = {213058}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/bti406}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/21/11/2739}, citeulike-linkout-2 = {http://www.ingentaconnect.com/content/oup/cabios/2005/00000021/00000011/art02739}, citeulike-linkout-3 = {http://view.ncbi.nlm.nih.gov/pubmed/15797909}, citeulike-linkout-4 = {http://www.hubmed.org/display.cgi?uids=15797909}, day = {1}, doi = {10.1093/bioinformatics/bti406}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {analysis, data, genein, mirna-xp, network}, month = {June}, number = {11}, pages = {2739--2747}, posted-at = {2008-09-27 07:41:22}, priority = {2}, publisher = {Oxford University Press}, title = {Modularized learning of genetic interaction networks from biological annotations and mRNA expression data}, url = {http://dx.doi.org/10.1093/bioinformatics/bti406}, volume = {21}, year = {2005} }

TY - JOUR ID - citeulike:213058 L3 - citeulike-article-id:213058 N2 - Motivation: Inferring the genetic interaction mechanism using Bayesian networks has recently drawn increasing attention due to its well-established theoretical foundation and statistical robustness. However, the relative insufficiency of experiments with respect to the number of genes leads to many false positive inferences. Results: We propose a novel method to infer genetic networks by alleviating the shortage of available mRNA expression data with prior knowledge. We call the proposed method modularized network learning' (MONET). Firstly, the proposed method divides a whole gene set to overlapped modules considering biological annotations and expression data together. Secondly, it infers a Bayesian network for each module, and integrates the learned subnetworks to a global network. An algorithm that measures a similarity between genes based on hierarchy, specificity and multiplicity of biological annotations is presented. The proposed method draws a global picture of inter-module relationships as well as a detailed look of intra-module interactions. We applied the proposed method to analyze Saccharomyces cerevisiae stress data, and found several hypotheses to suggest putative functions of unclassified genes. We also compared the proposed method with a whole-set-based approach and two expression-based clustering approaches. Availability: JAVA programs for the MONET framework are available from the corresponding author upon request. Web supplementary data is accessible at http://biosoft.kaist.ac.kr/~dhlee/monet/index.html Contact: doheon@kaist.ac.kr 10.1093/bioinformatics/bti406 IS - 11 JF - Bioinformatics SN - 1367-4803 EP - 2747 TI - Modularized learning of genetic interaction networks from biological annotations and mRNA expression data PB - Oxford University Press VL - 21 SP - 2739 KW - analysis KW - data KW - genein KW - mirna-xp KW - network AU - Lee, Phil AU - Lee, Doheon PY - 2005/06/01/ UR - http://dx.doi.org/10.1093/bioinformatics/bti406 ER -