Recurrent Neuro-fuzzy Network Models for Reverse Engineering Gene Regulatory Interactions

Understanding the way gene regulatory networks (complex systems of genes, proteins and other molecules) function and interact to carry out specific cell functions is currently one of the central goals in computational molecular biology. We propose an approach for inferring the complex causal relationships among genes from microarray experimental data based on a recurrent neuro-fuzzy method. The method derives information on the gene interactions in a highly interpretable form (fuzzy rules) and takes into account dynamical aspects of genes regulation through its recurrent structure. The gene interactions retrieved from a set of genes known to be highly regulated during the yeast cell-cycle are validated by biological studies, while our method surpasses previous computational techniques that attempted gene networks reconstruction, being able to retrieve significantly more biologically valid relationships among genes.

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