Comparative Analysis of MicroRNA-Target Gene Interaction Prediction Algorithms Based on Integrated P-Value Calculation

Abstract

In the paper we continue our previous research on microRNA- target gene prediction algorithms that has been already published in Krawczyk and Polańska [5] Comparative analysis of microRNA-target gene interaction prediction algorithms—the attempt to compare the results of three algorithms, Bioinformatics and Biomedical Engineering, Lecture Notes in Computer Science. MicroRNAs are non-coding molecules that consist approximately of 21–25 nucleotides. The crucial functions of miRNAs are: translational repression of the target genes and downregulation of the target genes. According to the modern knowledge about microRNAs, they bind to the gene in a specific place in the genome: 3’-UTR, 5’-UTR, CDS and Promoter. Moreover, they play an essential role in cancer growth. In these days we can observe a development of target prediction algorithms. A large number of prediction algorithms implicates many complications connected with the choice of the algorithm that will met the requirements of our experiment and that also predict the possible binding site of the microRNA to the target gene. Our goal is to create the method that can be useful for comparison of the microRNA-target gene interaction prediction algorithms that are based on different approaches of predicting the microRNA-target gene interactions. In order to achieve that, we decided to define one probability space for the mentioned algorithms. We performed the Fisher’s exact test to ensure that we can juxtapose the results from three different algorithms that take into consideration different aspects of binding microRNA to the target gene. Our research has strongly devel-oped from then and according to this, in this paper we would like to introduce our thorough study for not only the single microRNA molecule but for the microRNA family, as well.