title: Bayesian evolutionary hypergraph learning for predicting cancer clinical outcomes.
abstract: Predicting the clinical outcomes of cancer patients is a challenging task in biomedicine. A personalized and refined therapy based on predicting prognostic outcomes of cancer patients has been actively sought in the past decade. Accurate prognostic prediction requires higher-order representations of complex dependencies among genetic factors. However, identifying the co-regulatory roles and functional effects of genetic interactions on cancer prognosis is hindered by the complexity of the interactions. Here we propose a prognostic prediction model based on evolutionary learning that identifies higher-order prognostic biomarkers of cancer clinical outcomes. The proposed model represents the interactions of prognostic genes as a combinatorial space. It adopts a flexible hypergraph structure composed of a large population of hyperedges that encode higher-order relationships among many genetic factors. The hyperedge population is optimized by an evolutionary learning method based on sequential Bayesian sampling. The proposed learning approach effectively balances performance and parsimony of the model using information-theoretic dependency and complexity-theoretic regularization priors. Using MAQC-II project data, we demonstrate that our model can handle high-dimensional data more effectively than state-of-the-art classification models. We also identify potential gene interactions characterizing prognosis and recurrence risk in cancer.
authors: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea; Center for Biointelligence Technology (CBIT), Seoul National University, Seoul 151-742, Republic of Korea. Electronic address: sjkim@bi.snu.ac.kr.
authors: Soo-Jin
authors: SJ
authors: Kim
authors: Center for Biointelligence Technology (CBIT), Seoul National University, Seoul 151-742, Republic of Korea; School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Republic of Korea. Electronic address: jwha@bi.snu.ac.kr.
authors: Jung-Woo
authors: JW
authors: Ha
authors: Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Republic of Korea; Center for Biointelligence Technology (CBIT), Seoul National University, Seoul 151-742, Republic of Korea; School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Republic of Korea. Electronic address: btzhang@bi.snu.ac.kr.
authors: Byoung-Tak
authors: BT
authors: Zhang
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D007858:Learning; D009369:Neoplasms / Q000473:pathology / Q000628:therapy*; D016896:Treatment Outcome
keywords: Bayesian evolutionary learning; Cancer genomic data; Clinical outcome prediction; Hypergraph classifier