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Predicting DNA- and RNA-binding proteins from sequences with kernel methods. (English) Zbl 1402.92332
Summary: In this paper, support vector machines (SVMs) are applied to predict the nucleic-acid-binding proteins. We constructed two classifiers to differentiate DNA/RNA-binding proteins from non-nucleic-acid-binding proteins by using a conjoint triad feature which extract information directly from amino acids sequence of protein. Both self-consistency and jackknife tests show promising results on the protein datasets in which the sequences identity is less than 25%. In the self-consistency test, the predictive accuracy is 90.37% for DNA-binding proteins and 89.70% for RNA-binding proteins. In the jackknife test, the predictive accuracies are 78.93% and 76.75%, respectively. Comparison results show that our method is very competitive by outperforming other previously published sequence-based prediction methods.
Reviewer: Reviewer (Berlin)

MSC:
92D20 Protein sequences, DNA sequences
68T05 Learning and adaptive systems in artificial intelligence
62P10 Applications of statistics to biology and medical sciences; meta analysis
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