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

DALKIRAN A. , RİFAİOĞLU A. S. , Martin M. J. , Cetin-Atalay R., ATALAY M. V. , Dogan T.

BMC BIOINFORMATICS, vol.19, 2018 (Journal Indexed in SCI) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 19
  • Publication Date: 2018
  • Doi Number: 10.1186/s12859-018-2368-y
  • Title of Journal : BMC BIOINFORMATICS
  • Keywords: Protein sequence, EC numbers, Function prediction, Machine learning, Benchmark datasets, SUBFAMILY CLASS, ENZYMES


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.