Predicting Protein Localization Sites Using
an Ensemble Self-Labeled Framework

Emmanuel G Pintelas; Panagiotis Pintelas

doi:10.26717/BJSTR.2018.11.002066

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

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

DOI: 10.26717/BJSTR.2018.11.002066

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Abstract

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.