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Abstract: We explore whether protein-RNA interfaces differ from non-interfaces in terms of their structural features and whether structural features vary according to the type of the bound RNA (e.g., mRNA, siRNA, etc.), using a non-redundant dataset of 147 protein chains extracted from protein-RNA complexes in the Protein Data Bank. Furthermore, we use machine learning algorithms for training classifiers to predict protein-RNA interfaces using information derived from the sequence and structural features. We develop the Struct-NB classifier that takes into account structural information. We compare the performance of Naïve Bayes and Gaussian Naïve Bayes with that of Struct-NB classifiers on the 147 protein-RNA dataset using sequence and structural features respectively as input to the classifiers. The results of our experiments show that Struct-NB outperforms Naïve Bayes and Gaussian Naïve Bayes on the problem of predicting the protein-RNA binding interfaces in a protein sequence in terms of a range of standard measures for comparing the performance of classifiers.
Publication Title:
International Journal of Data Mining and Bioinformatics
Volume:
4
Issue:
1
Page Start:
21
Page End:
43
Peer Reviewed:
Yes
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Towfic, Fadi; Caragea, Cornelia; Gemperline, David; Dobbs, Drena & Honavar, Vasant.Struct-NB: Predicting Protein-RNA Binding Sites Using Structural Features,
article,
January 15, 2010;
[Geneva, Switzerland].
(https://digital.library.unt.edu/ark:/67531/metadc181676/:
accessed April 17, 2024),
University of North Texas Libraries, UNT Digital Library, https://digital.library.unt.edu;
crediting UNT College of Engineering.