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Avicenna Journal of Medical Biotechnology، جلد ۱۱، شماره ۱، صفحات ۱۰۴-۱۱۱
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عنوان انگلیسی Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers
چکیده انگلیسی مقاله Background: Nucleic acid-binding proteins play major roles in different biological processes, such as transcription, splicing and translation. Therefore, the nucleic acid-binding function prediction of proteins is a step toward full functional annotation of proteins. The aim of our research was the improvement of nucleic-acid binding function prediction. Methods: In the current study, nine machine-learning algorithms were used to predict RNA- and DNA-binding proteins and also to discriminate between RNA-binding proteins and DNA-binding proteins. The electrostatic features were utilized for prediction of each function in corresponding adapted protein datasets. The leave-one-out cross-validation process was used to measure the performance of employed classifiers. Results: Radial basis function classifier gave the best results in predicting RNA- and DNA-binding proteins in comparison with other classifiers applied. In discriminating between RNA- and DNA-binding proteins, multilayer perceptron classifier was the best one. Conclusion: Our findings show that the prediction of nucleic acid-binding function based on these simple electrostatic features can be improved by applied classifiers. Moreover, a reasonable progress to distinguish between RNA- and DNA-binding proteins has been achieved.
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نشانی اینترنتی http://www.ajmb.org/En/Article.aspx?id=10341
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زبان مقاله منتشر شده en
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