Prediction of RNA- and DNA-Binding Proteins Using Various Machine Learning Classifiers.

Mehdi Poursheikhali Asghari, Parviz Abdolmaleki

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Article in Avicenna journal of medical biotechnology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

0.5field-weighted citation impact, top 36% of its field

1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed, 9 citations in OpenAlex.

Interpretable prediction of nucleic acid-binding proteins using a protein language model.Bioinformatics advances · 2026
Article

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5 · Who and what money

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2 authors at 1 institution in 1 country.

Mehdi Poursheikhali AsghariDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.

Parviz AbdolmalekiDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran.

Tarbiat Modares University · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundNucleic 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.

methodsIn 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.

resultsRadial 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.

conclusionOur 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.

Indexed as

DNA-binding proteinsMachine-learning algorithmsRNA-binding proteins

Identifiers

PMID30800250

PMCPMC6359699

OpenAlexW2912503829

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LicenceCC BY

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