Introduction

MOTIVATION: The accurate prediction of disease status is a central challenge in clinical cancer research. Microarray-based gene biomarkers have been identified to predict outcome and outperform traditional clinical parameters. However, the robustness of the individual gene biomarkers is questioned because of their little reproducibility between different cohorts of patients. Substantial progress in treatment requires advances in methods to identify robust biomarkers. Several methods incorporating pathway information have been proposed to identify robust pathway markers and build classifiers at the level of functional categories rather than of individual genes. However, current methods consider the pathways as simple gene sets but ignore the pathway topological information, which is essential to infer a more robust pathway activity. RESULTS: Here, we propose a directed random walk (DRW)-based method to infer the pathway activity. DRW evaluates the topological importance of each gene by capturing the structure information embedded in the directed pathway network. The strategy of weighting genes by their topological importance greatly improved the reproducibility of pathway activities. Experiments on 18 cancer datasets showed that the proposed method yielded a more accurate and robust overall performance compared with several existing gene-based and pathway-based classification methods. The resulting risk-active pathways are more reliable in guiding therapeutic selection and the development of pathway-specific therapeutic strategies. AVAILABILITY: DRW is freely available at http://210.46.85.180:8080/DRWPClass/

Publications

  1. Topologically inferring risk-active pathways toward precise cancer classification by directed random walk.
    Cite this
    Liu W, Li C, Xu Y, Yang H, Yao Q, Han J, Shang D, Zhang C, Su F, Li X, Xiao Y, Zhang F, Dai M, Li X, 2013-09-01 - Bioinformatics (Oxford, England)

Credits

  1. Wei Liu
    Developer

    College of Bioinformatics Science and Technology, Harbin Medical University, China

  2. Chunquan Li
    Developer

  3. Yanjun Xu
    Developer

  4. Haixiu Yang
    Developer

  5. Qianlan Yao
    Developer

  6. Junwei Han
    Developer

  7. Desi Shang
    Developer

  8. Chunlong Zhang
    Developer

  9. Fei Su
    Developer

  10. Xiaoxi Li
    Developer

  11. Yun Xiao
    Developer

  12. Fan Zhang
    Developer

  13. Meng Dai
    Developer

  14. Xia Li
    Investigator

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Summary
AccessionBT006456
Tool TypeApplication
Category
PlatformsLinux/Unix
TechnologiesR
User InterfaceTerminal Command Line
Download Count0
Submitted ByXia Li