Multi-view feature selection for identifying gene markers: a diversified biological data driven approach.

ABSTRACT:

Background

In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population.

Results

In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we prop

SUBMITTER: Acharya S 

PROVIDER: S-EPMC7772934 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature