Integrating biological knowledge with gene expression profiles for survival prediction of cancer.

ABSTRACT: Due to the large variability in survival times between cancer patients and the plethora of genes on microarrays unrelated to outcome, building accurate prediction models that are easy to interpret remains a challenge. In this paper, we propose a general strategy for improving performance and interpretability of prediction models by integrating gene expression data with prior biological knowledge. First, we link gene identifiers in expression dataset with gene annotation databases such as Gene Ontology (GO). Then we construct "supergenes" for each gene category by summarizing information from genes related to outcome using a modified principal component analysis (PCA) method. Finally, instead of using genes as predictors, we use these supergenes representing information from each gene categ

SUBMITTER: Chen X 

PROVIDER: S-EPMC3198940 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature