Machine learning-based identification of genetic interactions from heterogeneous gene expression profiles.

ABSTRACT: The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene-gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets

SUBMITTER: Park C 

PROVIDER: S-EPMC6062065 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature