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Comput Biol Med. 2013 Oct;43(10):1502-11. doi: 10.1016/j.compbiomed.2013.07.024. Epub 2013 Aug 2.

MitProt-Pred: Predicting mitochondrial proteins of Plasmodium falciparum parasite using diverse physiochemical properties and ensemble classification.

Author information

  • 1Pattern Recognition Laboratory, Department of Computer and Information Sciences, PIEAS, Nilore, Islamabad, Pakistan.

Abstract

Mitochondrial protein of Plasmodium falciparum is an important target for anti-malarial drugs. Experimental approaches for detecting mitochondrial proteins are costly and time consuming. Therefore, MitProt-Pred is developed that utilizes Bi-profile Bayes, Pseudo Average Chemical Shift, Split Amino Acid Composition, and Pseudo Amino Acid Composition based features of the protein sequences. Hybrid feature space is also developed by combining different individual feature spaces. These feature spaces are learned and exploited through SVM based ensemble. MitProt-Pred achieved significantly improved prediction performance for two standard datasets. We also developed the score level ensemble, which outperforms the feature level ensemble.

© 2013 Elsevier Ltd. All rights reserved.

KEYWORDS:

Bi-profile Bayes; Ensemble classification; Mitochondrial proteins; Plasmodium falciparum; PseAAC; PseACS; SAAC; SVM

PMID:
24034742
[PubMed - indexed for MEDLINE]
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