Gene regulatory network reverse engineering using population based incremental learning and K-means

Authors:
Leon Palafox;Iba Hitoshi
Affiliations:
The University of Tokyo, Tokyo, Japan;The University of Tokyo, Tokyo, Japan
Venue:
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Year:
2012

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Abstract

Finding interactions among genes is one of the main problems in molecular biology. In this paper, we use a novel approach to model the gene's regulations, or Gene Regulatory Networks (GRNs). We use a Recursive Neural Network (RNN) to model the networks, and then use Population Based Incremental Learning (PBIL) enhanced with K-means to find the optimum parameters of the Neural Network. We present experiments with real data, we compare our algorithm with others approaches by calculating different statistics for the solutions.