Improving Rule Based Gene Regulatory Network Inference by means of Biclustering

GALLO, CRISTIAN A.; CARBALLIDO, JESSICA A.; PONZONI, IGNACIO

San Carlos de Bariloche

Conferencia; V Argenitinean Conference on Computational Biology and Bioinformatics (CAB2C 2014); 2014

Asociación Argentina de Bioinformática y Biología Computacional

algorithm for the inference of gene regulatory network. The method was validated with well known publicly available gene expression datasets. The results have shown that the combined approach infers a gene regulatory network with high average precision regarding the Yeasnet database, providing new relations that were not present in the GRN inferred by the rule based method alone. This show the importance of combining different approaches in the inference of gene regulatory network, since it provides alternative views of the data and allow the discovering of significant relations that may no be detectable by an specific approach. Further analysis is required in order to confirm these promissory results.