Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers
Author(s):
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
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.Language:
English
Published:
Avicenna Journal of Medical Biotechnology, Volume:11 Issue: 1, Jan -Mar 2019
Pages:
104 to 111
https://www.magiran.com/p1915657