Predicting protein-RNA interaction using sequence derived features and machine learning approach
by Chandan Pandey; Rokkam Sandeep; Aikansh Priyam; Satyajit Mahapatra; Sitanshu Sekhar Sahu
International Journal of Data Mining and Bioinformatics (IJDMB), Vol. 19, No. 3, 2017

Abstract: Protein-RNA interactions play a very crucial part in various cellular processes. Several computational methods are being developed based on primary, secondary and tertiary information of proteins and RNA to predict the interactions. In this paper, various sequence based information of proteins and RNA are explored to predict the interactions using machine learning approach. The conjoint ternion feature is found to be superior as compared to the other composition based features. It provides an accuracy of 89.67% and MCC of 0.79 on a standard database. When tested on an independent dataset, it provides the prediction accuracy of 83.23%.

Online publication date: Thu, 05-Apr-2018

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