title: Topologically inferring risk-active pathways toward precise cancer classification by directed random walk.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  DRW is freely available at http://210.46.85.180:8080/DRWPClass/
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Liu
authors.forename: Chunquan
authors.initials: C
authors.lastname: Li
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Xu
authors.forename: Haixiu
authors.initials: H
authors.lastname: Yang
authors.forename: Qianlan
authors.initials: Q
authors.lastname: Yao
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.forename: Desi
authors.initials: D
authors.lastname: Shang
authors.forename: Chunlong
authors.initials: C
authors.lastname: Zhang
authors.forename: Fei
authors.initials: F
authors.lastname: Su
authors.forename: Xiaoxi
authors.initials: X
authors.lastname: Li
authors.forename: Yun
authors.initials: Y
authors.lastname: Xiao
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.forename: Meng
authors.initials: M
authors.lastname: Dai
authors.forename: Xia
authors.initials: X
authors.lastname: Li
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics / Q000378:metabolism; D020869:Gene Expression Profiling; D006801:Humans; D009369:Neoplasms / Q000145:classification* / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis; D015203:Reproducibility of Results; D012306:Risk; D015398:Signal Transduction
chemical_list: D014408:Biomarkers, Tumor