Solving protein fold prediction problem using fusion of heterogeneous classifiers

Abdollah Dehzangi, Sasan Karamizadeh

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Protein fold prediction problem is considered as one of the most challenging tasks for molecular biology and Bioinformatics. In this study, a fusion of heterogeneous Meta classifiers namely: LogitBoost, Random Forest, and Rotation Forest, is proposed to solve this problem. The proposed approach aims at enhancing the protein fold prediction accuracy by enforcing diversity among its individual members by employing divers and accurate base classifiers. Our experimental results show that our proposed approach enhances the protein fold prediction accuracy using Ding and Dubchak's dataset and Dubchak and her coworkers' feature set, better than the previous works found in the literature.

Original languageEnglish (US)
Pages (from-to)3611-3621
Number of pages11
Issue number11
StatePublished - Nov 2011
Externally publishedYes

ASJC Scopus subject areas

  • Information Systems


  • Feature extraction
  • Fusion of heterogeneous classifiers
  • LogitBoost
  • Majority voting
  • Protein fold prediction problem
  • Random Forest
  • Rotation Forest


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