Struct-NB: Predicting Protein-RNA Binding Sites Using Structural Features
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The Department of Genetics, Development, and Cell Biology seeks to teach subcellular and cellular processes, genome dynamics, cell structure and function, and molecular mechanisms of development, in so doing offering a Major in Biology and a Major in Genetics.
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The Department of Genetics, Development, and Cell Biology was founded in 2005.
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- College of Agriculture and Life Sciences (parent college)
- College of Liberal Arts and Sciences (parent college)
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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.
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This is a manuscript of an article from International Journal of Data Mining and Bioinformatics 4 (2010): 21, doi: 10.1504/IJDMB.2010.030965. Posted with permission.