Please use this identifier to cite or link to this item: https://doi.org/10.1261/rna.5890304
Title: Prediction of RNA-binding proteins from primary sequence by a support vector machine approach
Authors: Han, L.Y. 
Cai, C.Z. 
Lo, S.L. 
Chung, M.C.M.
Chen, Y.Z. 
Keywords: mRNA
RNA-binding proteins
RNA-protein interactions
rRNA
snRNA
Support vector machine
tRNA
Issue Date: Mar-2004
Source: Han, L.Y., Cai, C.Z., Lo, S.L., Chung, M.C.M., Chen, Y.Z. (2004-03). Prediction of RNA-binding proteins from primary sequence by a support vector machine approach. RNA 10 (3) : 355-368. ScholarBank@NUS Repository. https://doi.org/10.1261/rna.5890304
Abstract: Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein-protein interactions. But insufficient attention has been paid to the prediction of protein-RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA-binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total of 4011 RNA-binding and 9781 non-RNA-binding proteins was used to train and test the SVM classification system, and an independent set of 447 RNA-binding and 4881 non-RNA-binding proteins was used to evaluate the classification accuracy. Testing results using this independent evaluation set show a prediction accuracy of 94.1%, 79.3%, and 94.1% for rRNA-, mRNA-, and tRNA-binding proteins, and 98.7%, 96.5%, and 99.9% for non-rRNA-, non-mRNA-, and non-tRNA-binding proteins, respectively. The SVM classification system was further tested on a small class of snRNA-binding proteins with only 60 available sequences. The prediction accuracy is 40.0% and 99.9% for snRNA-binding and non-snRNA-binding proteins, indicating a need for a sufficient number of proteins to train SVM. The SVM classification systems trained in this work were added to our Web-based protein functional classification software SVMProt, at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi. Our study suggests the potential of SVM as a useful tool for facilitating the prediction of protein-RNA interactions.
Source Title: RNA
URI: http://scholarbank.nus.edu.sg/handle/10635/53100
ISSN: 13558382
DOI: 10.1261/rna.5890304
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