JCD-DEA: a joint covariate detection tool for differential expression analysis on tumor expression profiles.

ABSTRACT:

Background

Differential expression analysis on tumor expression profiles has always been a key issue for subsequent biological experimental validation. It is important how to select features which best discriminate between different groups of patients. Despite the emergence of multivariate analysis approaches, prevailing feature selection methods primarily focus on multiple hypothesis testing on individual variables, and then combine them for an explanatory result. Besides, these methods, which are commonly based on hypothesis testing, view classification as a posterior validation of the selected variables.

Results

Based on previously provided A5 feature selection strategy, we develop a joint covariate detection tool for differential expression analysis on tumor expression p

SUBMITTER: Li Y 

PROVIDER: S-EPMC6599234 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature