Minimum redundancy maximum relevance feature selection approach for temporal gene expression data.

ABSTRACT:

Background

Feature selection, aiming to identify a subset of features among a possibly large set of features that are relevant for predicting a response, is an important preprocessing step in machine learning. In gene expression studies this is not a trivial task for several reasons, including potential temporal character of data. However, most feature selection approaches developed for microarray data cannot handle multivariate temporal data without previous data flattening, which results in loss of temporal information. We propose a temporal minimum redundancy - maximum relevance (TMRMR) feature selection approach, which is able to handle multivariate temporal data without previous data flattening. In the proposed approach we compute relevance of a gene by averaging F-statistic v

SUBMITTER: Radovic M 

PROVIDER: S-EPMC5209828 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature