Intra-relation reconstruction from inter-relation: miRNA to gene expression

In computational biology, a novel knowledge has been obtained mostly by identifying 'intra-relation,' the relation between entities on a specific biological level such as gene expression or miRNA, and many such researches have been successful. However, intra-relations are not fully elucidating complex cancer mechanism because the information that relations between miRNAs and target genes are strongly associated with different biological processes is missing. Here, we propose an integrated framework that combines genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression for the molecular-based classification of clinical outcomes. In order to demonstrate the validity of the proposed method, the prediction of short-term/long-term survival for 82 patients in glioblastoma multiforme (GBM) is adopted as a base task. Based on our results, the accuracy of our predictive model increases because of incorporation of information fused over genomic dataset from gene expression and genomic knowledge from inter-relation between miRNA and gene expression. In the present study, the intra-relation of gene expression was reconstructed from inter-relation between miRNA and gene expression for prediction of short-term/longterm survival of GBM patients. Our finding suggests that the utilization of external knowledge representing miRNA-mediated regulation of gene expression is substantially useful for elucidating the cancer phenotype.