Predicting protein-RNA interaction using sequence derived features and machine learning approach

Chandan Pandey; Rokkam Sandeep; Aikansh Priyam; Satyajit Mahapatra; Sitanshu Sekhar Sahu

doi:10.1504/IJDMB.2017.090991

Predicting protein-RNA interaction using sequence derived features and machine learning approach

Chandan Pandey, Rokkam Sandeep, Aikansh Priyam, Satyajit Mahapatra, Sitanshu Sekhar Sahu^*

^*Corresponding author for this work

School of Computer Engineering

Research output: Contribution to journal › Article › peer-review

2 Citations (Scopus)

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%.

Original language	English
Pages (from-to)	270-282
Number of pages	13
Journal	International Journal of Data Mining and Bioinformatics
Volume	19
Issue number	3
DOIs	https://doi.org/10.1504/IJDMB.2017.090991
Publication status	Published - 2017

All Science Journal Classification (ASJC) codes

Information Systems
General Biochemistry,Genetics and Molecular Biology
Library and Information Sciences

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10.1504/IJDMB.2017.090991

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title = "Predicting protein-RNA interaction using sequence derived features and machine learning approach",

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\%.",

author = "Chandan Pandey and Rokkam Sandeep and Aikansh Priyam and Satyajit Mahapatra and \{Sekhar Sahu\}, Sitanshu",

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AU - Pandey, Chandan

AU - Sandeep, Rokkam

AU - Priyam, Aikansh

AU - Mahapatra, Satyajit

AU - Sekhar Sahu, Sitanshu

PY - 2017

Y1 - 2017

N2 - 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%.

AB - 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%.

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JF - International Journal of Data Mining and Bioinformatics

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ER -

Predicting protein-RNA interaction using sequence derived features and machine learning approach

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