Gene regulatory networks modelling using a dynamic evolutionary hybrid.

ABSTRACT:

Background

Inference of gene regulatory networks is a key goal in the quest for understanding fundamental cellular processes and revealing underlying relations among genes. With the availability of gene expression data, computational methods aiming at regulatory networks reconstruction are facing challenges posed by the data's high dimensionality, temporal dynamics or measurement noise. We propose an approach based on a novel multi-layer evolutionary trained neuro-fuzzy recurrent network (ENFRN) that is able to select potential regulators of target genes and describe their regulation type.

Results

The recurrent, self-organizing structure and evolutionary training of our network yield an optimized pool of regulatory relations, while its fuzzy nature avoids noise-related probl

SUBMITTER: Maraziotis IA 

PROVIDER: S-EPMC2848237 | biostudies-literature | 2010 Mar

REPOSITORIES: biostudies-literature