A Machine Learning Holistic Strategy for miRNA-mRNA Module Discovery

Abstract

microRNA (abbreviated miRNA) is a small non-coding molecule, that is made up of approximately 22 nucleotides, found in plants, animals, and some viruses. They act as regulators by binding their targets to degrade or suppress the translation of their transcripts. Therefore miRNAs plays an important role in gene regulatory networks, and an improved understanding of miRNAs will widen our knowledge of these networks and their relation with diseases. A single miRNA targets multiple mRNAs, and a single mRNA is targeted by multiple miRNAs to develop a many-to-many relations, known as miRNAm- RNA module. Some methods have ignored this relation, by just focusing on miRNA-mRNA pairs. However current methods evolved to consider this relation but unfortunately they focused on some part of the data analyzed, and ignored the other. Unfortunately, they still leave open issues, but the higher benefit is that they provided results, that opened issue of the possibility of widening the scope of module discovery, so as to be extended to a wider disease spectrum, where miRNA-mRNA interactions play a relevant role. The research proposes a holistic procedure for miRNA-mRNA module identification that exploits as much data as possible. It uses machine learning and mathematical approaches to aid in the analysis and implementation. We adopt the strategy of postponing any decision until biological results are exploited. Many statistical tests have been diverted into specially-devised evolving metrics, for sake of possible solutions. Consequently, the Implementation on High Performance Computing (HPC) is crucial, since this strategy is rather expensive in terms of computation. Fortunately, it allows the discovery of modules whose miRNAs and mRNAs are not differentially expressed and the discovery miRNA targets not yet considered, as well. In this research, the procedure, is implemented on a Multiple Myeloma dataset publicly available on Gene Expression Omnibus (GEO) platform, as a case study of diseases, specifically as a cancer instance analysis, and scout some biological issues. The procedure has introduced novel strategies for miRNA-mRNA module discovery. Main achievements of this thesis work are: 1)we introduce a novel strategy for miRNA-mRNA module discovery; 2) we establish an unprecedented way of jointly using using many metrics to find new links between miRNA and mRNA clusters involving non differentially expressed RNA pairs as well; 3) and finally, we highlight new miRNA-mRNA interactions with a methodology that can be extended to a wide spectrum of diseases.