Comparison of evolutionary algorithms in gene regulatory network model inference.

ABSTRACT:

Background

The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.

Results

This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer

SUBMITTER: Sirbu A 

PROVIDER: S-EPMC2831005 | biostudies-literature | 2010 Jan

REPOSITORIES: biostudies-literature