NDLI: Integration of pathway structure information into a reweighted partial Cox regression approach for survival analysis on high-dimensional gene expression data.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Liu, Wei Wang, Qiuyu Zhao, Jianmei Zhang, Chunlong Liu, Yuejuan Zhang, Jian Bai, Xuefeng Li, Xuecang Feng, Houming Liao, Mingzhi Wang, Wei Li, Chunquan
Description	Country affiliation: China Author Affiliation: Liu W ( Department of Mathematics, Heilongjiang Institute of Technology, Harbin, 150050, China. 45957738@qq.com.)
Abstract	Accurately predicting the risk of cancer relapse or death is important for clinical utility. The emerging high-dimensional gene expression data provide the opportunity as well as the challenge to uncover the relationship between gene expression and censored survival outcome. While several Cox models have been proposed to deal with high-dimensional covariates and censored continuous survival data, they usually generalize poorly to independent datasets. Most methods build the Cox model exclusively on gene expression data, but ignore the molecular interaction relation among genes, which has been successfully integrated into molecular classification with categorical outcomes and improved predictive performance. Here, we integrate gene-interaction information into a Cox model and propose a reweighted partial Cox regression (RPCR) approach in order to accurately predict the risk of cancer events. RPCR improves the predictive accuracy and generalization of a Cox model by promoting genes with large topological importance, which is evaluated by a directed random walk in a reconstructed global pathway graph. We applied RPCR to the survival prediction of two cancer types and used two concordance statistic measures to assess the prediction performance. Both within-dataset experiments and cross-dataset experiments showed that RPCR could predict the risk of patients with higher accuracy and greater robustness.
File Format	HTM / HTML
ISSN	1742206X
Issue Number	7
Volume Number	11
e-ISSN	17422051
Journal	Molecular BioSystems
Language	English
Publisher	Royal Society of Chemistry
Publisher Date	2015-07-01
Publisher Place	Great Britain (UK)
Access Restriction	Subscribed
Subject Keyword	Discipline Molecular Biology Discipline Biochemistry Breast Neoplasms Mortality Glioblastoma Transcriptome Adult Aged Genetics Metabolism Female Humans Kaplan-meier Estimate Male Middle Aged Proportional Hazards Models Risk Statistics, Nonparametric Journal Article Research Support, Non-u.s. Gov't
Content Type	Text
Resource Type	Article
Subject	Molecular Biology Biotechnology

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