Gene regulatory networks modelling using a dynamic evolutionary hybrid

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 multilayer 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 problems. Furthermore, we are able to assign scores for each regulation, highlighting the confidence in the retrieved relations. The approach was tested by applying it to several benchmark datasets of yeast, manag...

• Type: Journal
• Year: 2010
• Where: BMCBI
• Authors: Ioannis A. Maraziotis, Andrei Dragomir, Dimitris Thanos
• Keywords: BMCBI 2010, Gene Regulatory Networks, Regulatory Networks, Regulatory Relations