Using machine learning to identify gene interaction networks associated with breast cancer.

ABSTRACT:

Background

Breast cancer (BC) is one of the most prevalent cancers worldwide but its etiology remains unclear. Obesity is recognized as a risk factor for BC, and many obesity-related genes may be involved in its occurrence and development. Research assessing the complex genetic mechanisms of BC should not only consider the effect of a single gene on the disease, but also focus on the interaction between genes. This study sought to construct a gene interaction network to identify potential pathogenic BC genes.

Methods

The study included 953 BC patients and 963 control individuals. Chi-square analysis was used to assess the correlation between demographic characteristics and BC. The joint density-based non-parametric differential interaction network analysis and classification

SUBMITTER: Liu L 

PROVIDER: S-EPMC9575346 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature