Protein-RNA Interaction Prediction Using Graphical Representation of Biological Sequences

Abstract

Protein-RNA interactions play a crucial role in posttranscriptional regulation of gene expression and have diverse functions in various biological processes. Therefore, identification of protein-RNA interactions is quite important. Experimental methods used for this purpose are expensive, time-consuming and labor intensive. Alternatively, machine learning based methods are proposed to detect protein-RNA interactions computationally. In these methods, each protein-RNA pair is represented by a feature vector which is then used to train machine learning methods. Here, in this study, we also proposed an alternative method to form a feature vector for each protein-RNA pair. Compared to the existing methods, the proposed method creates low-dimensional feature vectors which in turn decreases the overall computational time required to train and test the machine learning methods. Moreover, the proposed method does not make any concession on the classification performance.