Incorporating prior knowledge into Gene Network Study

• DOI: 10.1093/bioinformatics/btt443
• Publication Date: 2013-08-17T00:29:16Z
• Authors: Zixing Wang; Wenlong Xu; F. Anthony San Lucas; Yin Liu
• Keywords: Interpretability; KEGG; Gene regulatory network; Lasso; Complement

A major goal in genomic research is to identify genes that may jointly influence a biological response. From many years of intensive biomedical research, large body knowledge, or pathway information, has accumulated available databases. There strong interest leveraging these pathways improve the statistical power and interpretability studying gene networks associated with complex phenotypes. This prior information valuable complement large-scale data such as expression generated from microarrays. However, it non-trivial task effectively integrate knowledge into when reconstructing networks.In this article, we developed applied Lasso method Bayesian perspective, call (pLasso), for reconstruction networks. In method, partition edges between two subsets: one subset present known pathways, whereas other no associated. Our assigns different distributions each according modified criterion incorporates on both network structure information. Simulation studies have indicated more effective recovering underlying than traditional does not use We pLasso microarray datasets, where used Pathway Commons (PC) Kyoto Encyclopedia Genes Genomes (KEGG) reconstruction, successfully identified hub clinical outcome cancer patients.The source code at http://nba.uth.tmc.edu/homepage/liu/pLasso.