ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature

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10.1186s12859-018-2368-y.pdf

Date

2018-09-21

Author

Dalkıran, Alperen
Rifaioğlu, Ahmet Süreyya
Dogan, Tunca
Atalay, Mehmet Volkan

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Background: The automated prediction of the enzymatic functions of uncharacterized proteins is a crucial topic in bioinformatics. Although several methods and tools have been proposed to classify enzymes, most of these studies are limited to specific functional classes and levels of the Enzyme Commission (EC) number hierarchy. Besides, most of the previous methods incorporated only a single input feature type, which limits the applicability to the wide functional space. Here, we proposed a novel enzymatic function prediction tool, ECPred, based on ensemble of machine learning classifiers.

Subject Keywords

Protein sequence, EC numbers, Function prediction, Machine learning, Benchmark datasets

URI

https://hdl.handle.net/11511/30698

Journal

BMC BIOINFORMATICS

DOI

https://doi.org/10.1186/s12859-018-2368-y

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Graduate School of Natural and Applied Sciences, Article

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Citation Formats

A. Dalkıran, A. S. Rifaioğlu, T. Dogan, and M. V. Atalay, “ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature,” BMC BIOINFORMATICS, pp. 0–0, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/30698.