Work

A Local Genetic Algorithm for the Identification of Condition‐Specific MicroRNA‐Gene Modules

Year: 2013

Type: article

Abstract: Transcription factor and microRNA are two types of key regulators of gene expression. Their regulatory mechanisms are highly complex. In this study, we propose a computational method to predict condition-specific regulatory modules that consist of microRNAs, transcription factors, and their commonly regulated genes. We used matched global expression profiles of mRNAs and microRNAs together with the predicted targets of transcription factors and microRNAs to construct an underlying regulatory network. Our method searches for highly scored modules from the network based on a two-step heuristic method that combines genetic and local search algorithms. Using two matched expression datasets, we demonstrate that our method can identify highly scored modules with statistical significance and biological relevance. The identified regulatory modules may provide useful insights on the mechanisms of transcription factors and microRNAs.

Source: The Scientific World JOURNAL

DOI: 10.1155/2013/197406

Authors: Wenbo Mu, Damian Roqueiro, Yang Dai

Institution: University of Illinois Chicago

Language: English

Topic: Gene Regulatory Network Analysis

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $860

Funder: University of Illinois at Urbana-Champaign