Layered Approach for Predicting Protein Subcellular Localization in Yeast Microarray Data

A  Kumaravel   and R  Pradeepa

doi:10.17485/ijst/2013/v6isp5.13

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Layered Approach for Predicting Protein Subcellular Localization in Yeast Microarray Data

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2013/v6isp5.13

Year: 2013, Volume: 6, Issue: Supplementary 5, Pages: 1-5

Original Article

Layered Approach for Predicting Protein Subcellular Localization in Yeast Microarray Data

A. Kumaravel^1* and R. Pradeepa²

¹ Professor and Dean, Department of Computer Science and Engineering, Bharath University, [email protected]
²PG Student, Department of Computer Science and Engineering, [email protected]
*Author for Correspondence
A. Kumaravel
Department of Computer Science and Engineering, Bharath University,
Email: [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Subcellular localization is a well-designed representation of proteins. We need a fully automatic and reliable prediction system for protein subcellular localization, especially for the analysis of large-scale of yeast microarray data. In this paper we consider the dataset with multi classes and propose the classification for each location of protein subcellular in a separate layer. In this work, a multi-classification approach for subcellular localization is designed and developed to achieve high efficiency and improve the prediction and classification accuracy. The rule based Ripper method has been found to predict the subcellular localization of proteins from their protein microarray data, compared to other classifiers.
Keywords: Data Mining, Microarray, Classification, Layered Approach, Protein Subcellular Localization.