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Gram - positive and gram - negative subcellular localization using rotation forest and physicochemical-based features

Dehzangi, A. and Sohrabi, S. and Heffernan, R. and Sharma, Alokanand and Lyons, J. and Paliwal, K.K. and Sattar, A. (2015) Gram - positive and gram - negative subcellular localization using rotation forest and physicochemical-based features. BMC Bioinformatics, 16 (S1). pp. 1-12. ISSN 1471-2105

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    Abstract

    The functioning of a protein relies on its location in the cell. Therefore, predicting protein subcellular localization is an important step towards protein function prediction. Recent studies have shown that relying on Gene Ontology (GO) for feature extraction can improve the prediction performance. However, for newly sequenced proteins, the GO is not available. Therefore, for these cases, the prediction performance of GO based methods degrade significantly. Results: In this study, we develop a method to effectively employ physicochemical and evolutionary-based information in the protein sequence. To do this, we propose segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids to tackle Gram-positive and Gram-negative subcellular localization. We explore our proposed feature extraction techniques using 10 attributes that have been experimentally selected among a wide range of physicochemical attributes. Finally by applying the Rotation Forest classification technique to our extracted features, we enhance Gram-positive and Gram-negative subcellular localization accuracies up to 3.4% better than previous studies which used GO for feature extraction. Conclusion: By proposing segmentation based feature extraction method to explore potential discriminatory information based on physicochemical properties of the amino acids as well as using Rotation Forest classification technique, we are able to enhance the Gram-positive and Gram-negative subcellular localization prediction accuracies, significantly.

    Item Type: Journal Article
    Additional Information: This paper was presented at the 9th IAPR conference on Pattern Recognition in Bioinformatics. This is entered in the repository as a journal article.
    Subjects: T Technology > T Technology (General)
    Divisions: Faculty of Science, Technology and Environment (FSTE) > School of Engineering and Physics
    Depositing User: Alokanand Sharma
    Date Deposited: 13 Jan 2016 10:49
    Last Modified: 02 Jun 2016 11:53
    URI: http://repository.usp.ac.fj/id/eprint/8643
    UNSPECIFIED

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