Increasing the Target Prediction Accuracy of MicroRNA Based on Combination of Prediction Algorithms

MicroRNA is an oligonucleotide that plays a role in the pathogenesis of several diseases (mentioning Cancer). It is a non-coding RNA that is involved in the control of gene expression through the binding and inhibition of mRNA. In this study, three algorithms were implemented in WEKA software using two testing modes to analyze five datasets of miRNA families. The data mining techniques are used to compare the interactions of miRNA-mRNA that it either belongs to the same gene-family or to different families, and to establish a biological scheme that explains how the biological parameters are involved or less involved in miRNA-mRNA prediction. The factors that were involved in the prediction process includs match, mismatch, bulge, loop, and score to represent the binding characteristics, while the position, 3’UTR length, and chromosomal location and chromosomal categorizations represent the characteristics of the target mRNA. These attributes can provide an empirical guidance for study of specific miRNA family to scan the whole human genome for novel targets. This research provides promising results that can be utilized for current and future research in this field.