Comparative analysis of microRNA-target gene interaction prediction algorithms - the attempt to compare the results of three algorithms

MicroRNAs are non-coding, small molecules (21–25 nucleotides). They regulate gene expression by downregulation of the target gene or translational repression. What is more, they are involved in cancer growth. Nowadays, we can observe a sustainable growth and development of computational target prediction programs. The plethora of prediction algorithms cause the problem with a choice of the one algorithm, that may give satisfactory results– the possible binding site of the microRNA to the target gene. What is crucial is that the result is considered as satisfactory one, when it is statistically significant and there is a high probability that a specific gene is the target of real microRNA. In order to compare the results obtained from different algorithms we have to define one probability space for each of them. We performed a proper statistical test (Fisher’s exact test) to ensure that we can juxtapose the results from three different algorithms which take into account different aspects of binding microRNA to the target gene. The conclusion of our work is the suggestion of the way in which one can juxtapose the results from algorithms based on different methods of prediction the possible miRNA-target gene interactions.