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Avicenna Journal of Medical Biotechnology، جلد ۱۱، شماره ۱، صفحات ۱۰۴-۱۱۱
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers |
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| 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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| کلیدواژه‌های انگلیسی مقاله |
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| نویسندگان مقاله |
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| نشانی اینترنتی |
http://www.ajmb.org/En/Article.aspx?id=10341 |
| فایل مقاله |
اشکال در دسترسی به فایل - ./files/site1/rds_journals/133/article-133-1975024.pdf |
| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
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| نوع مقاله منتشر شده |
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