A consensus multi-view multi-objective gene selection approach for improved sample classification.

ABSTRACT:

Background

In the field of computational biology, analyzing complex data helps to extract relevant biological information. Sample classification of gene expression data is one such popular bio-data analysis technique. However, the presence of a large number of irrelevant/redundant genes in expression data makes a sample classification algorithm working inefficiently. Feature selection is one such high-dimensionality reduction technique that helps to maximize the effectiveness of any sample classification algorithm. Recent advances in biotechnology have improved the biological data to include multi-modal or multiple views. Different 'omics' resources capture various equally important biological properties of entities. However, most of the existing feature selection methodologies are

SUBMITTER: Acharya S 

PROVIDER: S-EPMC7495900 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature