A robust fuzzy rule based integrative feature selection strategy for gene expression data in TCGA.

ABSTRACT: BACKGROUND:Lots of researches have been conducted in the selection of gene signatures that could distinguish the cancer patients from the normal. However, it is still an open question on how to extract the robust gene features. METHODS:In this work, a gene signature selection strategy for TCGA data was proposed by integrating the gene expression data, the methylation data and the prior knowledge about cancer biomarkers. Different from the traditional integration method, the expanded 450 K methylation data were applied instead of the original 450 K array data, and the reported biomarkers were weighted in the feature selection. Fuzzy rule based classification method and cross validation strategy were applied in the model construction for performance evaluation. RESULTS:Our selected gene feat

SUBMITTER: Fan S 

PROVIDER: S-EPMC6357346 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature