Improvement of GNs inference through biological data integration

A current challenge in gene annotation is to define the gene function in the context of the network of relationships instead of using gene or their products alone. In this way the gene networks (GNs) inference has emerged as an approach to better understand the biology of the system. In recent years there has been a growing of use of other biological information than expression data to better recover the gene networks. These approaches are called data integration. Although several works in data integration have increased the performance of network inference, the precise gain of adding each type of biological information is still unclear. In this work we propose a methodology to include biological information into an inference algorithm, in order to assess its prediction gain by using biological information and expression profile together. Our results shows, as expected, that by adding biological information is a very important approach for the improvement of inference. The sensitivity measure presented approximately 90% of correct recovering, by setting equal weights for biological and expression profile. The PPV measure indicates that is a very difficult task due to the complexity of the biological machinery and the indirect relationship between transcripts and proteins. In addition, it could be observed a logarithmic behavior of the sensitivity measure. This work presents a first step towards assessing the gain in adding prior biological information in the inference of gene networks by considering an eukaryote (P. falciparum) organism.