Discovery of microRNA-mRNA modules via population-based probabilistic learning. Export

@article{citeulike:1160512, abstract = {MOTIVATION: MicroRNAs (miRNAs) and mRNAs constitute an important part of gen regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e., computationally predicted target genes of miRNAs and their respective expression profiles. RESULTS: We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways.}, address = {Center for Bioinformation Technology, Seoul National University, Seoul 151-742, Korea, School of Computing, Soongsil University, Seoul 156-743, Korea, and School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea.}, author = {Joung, Je-Gun G. and Hwang, Kyu-Baek B. and Nam, Jin-Wu W. and Kim, Soo-Jin J. and Zhang, Byoung-Tak T.}, citeulike-article-id = {1160512}, citeulike-linkout-0 = {http://dx.doi.org/10.1093/bioinformatics/btm045}, citeulike-linkout-1 = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/23/9/1141}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/17350973}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=17350973}, day = {9}, doi = {10.1093/bioinformatics/btm045}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {cancer, human, microrna, mrna, target}, month = {March}, posted-at = {2007-05-10 10:19:44}, priority = {2}, title = {Discovery of microRNA-mRNA modules via population-based probabilistic learning.}, url = {http://dx.doi.org/10.1093/bioinformatics/btm045}, year = {2007} }

TY - JOUR ID - citeulike:1160512 L3 - citeulike-article-id:1160512 N2 - MOTIVATION: MicroRNAs (miRNAs) and mRNAs constitute an important part of gen regulatory networks, influencing diverse biological phenomena. Elucidating closely related miRNAs and mRNAs can be an essential first step towards the discovery of their combinatorial effects on different cellular states. Here, we propose a probabilistic learning method to identify synergistic miRNAs involving regulation of their condition-specific target genes (mRNAs) from multiple information sources, i.e., computationally predicted target genes of miRNAs and their respective expression profiles. RESULTS: We used data sets consisting of miRNA-target gene binding information and expression profiles of miRNAs and mRNAs on human cancer samples. Our method allowed us to detect functionally correlated miRNA-mRNA modules involved in specific biological processes from multiple data sources by using a balanced fitness function and efficient searching over multiple populations. The proposed algorithm found two miRNA-mRNA modules, highly correlated with respect to their expression and biological function. Moreover, the mRNAs included in the same module showed much higher correlations when the related miRNAs were highly expressed, demonstrating our method's ability for finding coherent miRNA-mRNA modules. Most members of these modules have been reported to be closely related with cancer. Consequently, our method can provide a primary source of miRNA and target sets presumed to constitute closely related parts of gene regulatory pathways. JF - Bioinformatics SN - 1460-2059 AD - Center for Bioinformation Technology, Seoul National University, Seoul 151-742, Korea, School of Computing, Soongsil University, Seoul 156-743, Korea, and School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea. TI - Discovery of microRNA-mRNA modules via population-based probabilistic learning. KW - cancer KW - human KW - microrna KW - mrna KW - target AU - Joung, Je-Gun AU - Hwang, Kyu-Baek AU - Nam, Jin-Wu AU - Kim, Soo-Jin AU - Zhang, Byoung-Tak PY - 2007/03/09/ UR - http://dx.doi.org/10.1093/bioinformatics/btm045 ER -