title: Integrative Pathway Analysis Using Graph-Based Learning with Applications to TCGA Colon and Ovarian Data.
abstract: Recent method development has included multi-dimensional genomic data algorithms because such methods have more accurately predicted clinical phenotypes related to disease. This study is the first to conduct an integrative genomic pathway-based analysis with a graph-based learning algorithm. The methodology of this analysis, graph-based semi-supervised learning, detects pathways that improve prediction of a dichotomous variable, which in this study is cancer stage. This analysis integrates genome-level gene expression, methylation, and single nucleotide polymorphism (SNP) data in serous cystadenocarcinoma (OV) and colon adenocarcinoma (COAD). The top 10 ranked predictive pathways in COAD and OV were biologically relevant to their respective cancer stages and significantly enhanced prediction accuracy and area under the ROC curve (AUC) when compared to single data-type analyses. This method is an effective way to simultaneously predict binary clinical phenotypes and discover their biological mechanisms.
authors.affiliation: Department of Mathematics and Statistics, Elon University, Elon, NC, USA. ; Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
authors.forename: Andrew E
authors.initials: AE
authors.lastname: Dellinger
authors.affiliation: Department of Medicine, Division of Medical Oncology, Duke University School of Medicine, Durham, NC, USA.
authors.forename: Andrew B
authors.initials: AB
authors.lastname: Nixon
authors.affiliation: Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA. ; School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China.
authors.forename: Herbert
authors.initials: H
authors.lastname: Pang
keywords: clinical outcome prediction; colon adenocarcinoma; integrative analysis; multi-dimensional genomic data; serous cystadenocarcinoma