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

Dalkıran A., Rifaioğlu A. S., Martin M. J., Cetin-Atalay R., Atalay M. V., Dogan T.

BMC BIOINFORMATICS, cilt.19, 2018 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 19
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1186/s12859-018-2368-y
  • Dergi Adı: BMC BIOINFORMATICS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Anahtar Kelimeler: Protein sequence, EC numbers, Function prediction, Machine learning, Benchmark datasets, SUBFAMILY CLASS, ENZYMES
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

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.