A feature selection approach for evaluate the inference of GRNs through biological data integration-A case study on A. Thaliana

Abstract

The inference of gene regulatory networks (GRNs) from expression profiles is a great challenge in bioinformatics due to the curse of dimensionality. For this reason, several methods that perform data integration have been developed to reduce the estimation error of the inference. However, it is not completely formulated how to use each type of biological information available. This work address this issue by proposing feature selection approach in order to integrate biological data and evaluate three types of biological information regarding their effect on the similarity of inferred GRNs. The proposed feature selection method is based on sequential forward floating selection (SFFS) search algorithm and the mean conditional entropy (MCE) as criterion function. An expression dataset was built as an additional contribution of this work containing 22746 genes and 1206 experiments regarding A. thaliana. The experimental results achieve 39% of GRNs improvement in average when compared to non-use of biological data integration. Besides, the results showed that the improvement is associated to a specific type of biological information: the cellular localization, which is a valuable and information for the development of new experiments and indicates an important insight for investigation.