TY - JOUR T1 - A Bayesian network approach for modeling mixed features in TCGA ovarian cancer data JF - bioRxiv DO - 10.1101/033332 SP - 033332 AU - Qingyang Zhang AU - Ji-Ping Wang AU - Northwestern PSOC members Y1 - 2015/01/01 UR - http://biorxiv.org/content/early/2015/11/30/033332.abstract N2 - We propose an integrative framework to select important genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a logistic Bayesian network model based on The Cancer Genome Atlas data. The constructed Bayesian network has identified four gene clusters of distinct cellular functions, 13 driver genes, as well as some new biological pathways which may shed new light into the molecular mechanisms of ovarian cancer. ER -