Feature selection algorithm based on dual correlation filters for cancer-associated somatic variants.

ABSTRACT:

Background

Since the development of sequencing technology, an enormous amount of genetic information has been generated, and human cancer analysis using this information is drawing attention. As the effects of variants on human cancer become known, it is important to find cancer-associated variants among countless variants.

Results

We propose a new filter-based feature selection method applicable for extracting cancer-associated somatic variants considering correlations of data. Both variants associated with the activation and deactivation of cancer's characteristics are analyzed using dual correlation filters. The multiobjective optimization is utilized to consider two types of variants simultaneously without redundancy. To overcome high computational complexity problem, we

SUBMITTER: Seo H 

PROVIDER: S-EPMC7596964 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature