Relevant and Non-Redundant Feature Selection for Cancer Classification and Subtype Detection.

ABSTRACT: Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) d

SUBMITTER: Rana P 

PROVIDER: S-EPMC8428340 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature