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.
