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Research ArticleOpen Access

Predicting Protein Localization Sites Using an Ensemble Self-Labeled Framework

Volume 11 - Issue 2

Emmanuel G Pintelas1 and Panagiotis Pintelas*2

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    • 1Department of Electrical & Computer Engineering, University of Patras, Greece
    • 2Department of Mathematics, University of Patras, Greece
    • *Corresponding author: Panagiotis Pintelas, Department of Mathematics, University of Patras, Greece

Received: November 05, 2018;   Published: November 19, 2018

DOI: 10.26717/BJSTR.2018.11.002066

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In recent years machine learning has been thoroughly used in the bioinformatics and biomedical field. The prediction of cellular localization of the proteins can be considered very significant task in bioinformatics since wrong localization site can cause various diseases and infections to humans. Ensemble learning algorithms and semi-supervised algorithms have been independently developed to build efficient and robust classification models. In this paper we focus on the prediction of protein localization site in Escherichia Coli and Saccharomyces cerevisiae organisms utilizing a semi-supervised self-labeled algorithm based on ensemble methodologies. The experimental results showed the efficiency of our proposed algorithm compared against state-of-the-art self-labeled techniques.

Introduction| Proposed Methodology| Experimental Results| Conclusion| References|