Analyzing a co-occurrence gene-interaction network to identify disease-gene association.

ABSTRACT:

Background

Understanding the genetic networks and their role in chronic diseases (e.g., cancer) is one of the important objectives of biological researchers. In this work, we present a text mining system that constructs a gene-gene-interaction network for the entire human genome and then performs network analysis to identify disease-related genes. We recognize the interacting genes based on their co-occurrence frequency within the biomedical literature and by employing linear and non-linear rare-event classification models. We analyze the constructed network of genes by using different network centrality measures to decide on the importance of each gene. Specifically, we apply betweenness, closeness, eigenvector, and degree centrality metrics to rank the central genes of the networ

SUBMITTER: Al-Aamri A 

PROVIDER: S-EPMC6368766 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature