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• W2912503829 abstract "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.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.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.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." @default.
• W2912503829 created "2019-02-21" @default.
• W2912503829 creator A5006228503 @default.
• W2912503829 creator A5024089857 @default.
• W2912503829 date "2019-02-26" @default.
• W2912503829 modified "2023-09-23" @default.
• W2912503829 title "Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers." @default.
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• W2912503829 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6359699" @default.
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• W2912503829 hasPublicationYear "2019" @default.
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