MiXGENE Tool for Learning from Heterogeneous Gene Expression Data Using Prior Knowledge

Abstract

High-throughput genomic technologies have proved to be useful in the search for both genetic disease markers and more complex predictive and descriptive models. By the same token, it became obvious that accurate and interpretable models need to concern more than raw measurements taken at a single phase of gene expression. In order to reach a deeper understanding of the molecular nature of complexly orchestrated biological processes, all the available measurements and existing genomic knowledge need to be fused. In this paper, we introduce a tool for machine learning from heterogeneous gene expression data using prior knowledge. The tool is called miXGENE, it is elaborated upon in close connection with the biological departments that dispose of the above-mentioned data and have a strong interest in their integration within particular problem-oriented projects. The main idea is not merely to capture the transcriptional phase of gene expression quantified by the amount of messenger RNA (mRNA). The increasing availability of microRNA (miRNA) data asks for its concurrent analysis with the transcriptional data. Moreover, epigenetic data such as methylation measurements can help to explain unexpected transcriptional irregularities. miXGENE is an environment for building workflows that enable rapid prototyping of integrative molecular models.