Using Machine Learning to Measure Relatedness Between Genes: A Multi-Features Model.

ABSTRACT: Measuring conditional relatedness between a pair of genes is a fundamental technique and still a significant challenge in computational biology. Such relatedness can be assessed by gene expression similarities while suffering high false discovery rates. Meanwhile, other types of features, e.g., prior-knowledge based similarities, is only viable for measuring global relatedness. In this paper, we propose a novel machine learning model, named Multi-Features Relatedness (MFR), for accurately measuring conditional relatedness between a pair of genes by incorporating expression similarities with prior-knowledge based similarities in an assessment criterion. MFR is used to predict gene-gene interactions extracted from the COXPRESdb, KEGG, HPRD, and TRRUST databases by the 10-fold cross validatio

SUBMITTER: Wang Y 

PROVIDER: S-EPMC6414665 | biostudies-literature | 2019 Mar

REPOSITORIES: biostudies-literature