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Layered Approach for Predicting Protein Subcellular Localization in Yeast Microarray Data


Affiliations
1 Department of Computer Science and Engineering, Bharath University, Selaiyur, Chennai-600073, India
2 Department of Computer Science and Engineering, Bharath University, Selaiyur, Chennai - 600073
 

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
User

  • Hua S, and Sun Z (2001). Support vector machine approach for protein subcellular localization prediction, Bioinformatics, vol 17(8), 721–728, Available From: http://bioinformatics.oxfordjournals.org/
  • Available From: http://videocast.nih.gov/pdf/rm/Snyder.pdf
  • Gifty P, and Ravichandran J M et al. (2012). Efficient classifier for R2L and U2R attacks, International Journal of Computer Applications, vol 45, No. 21, 0975–8887.
  • Kumaravel A, and Niraisha M (2013). Multi-classification approach for detecting network attacks ICT545, 2013-IEEE Conference on Information and Communication Technologies.
  • Available From: Weka: http://www.cs.waikato.ac.nz/~ml/weka/
  • Protein localization dataset from Institute of Molecular and Cellular Biology Osaka, University, Available From: http://www.imcb.osaka-u.ac.jp/nakai/psort.html
  • Gaur A, and Richariya V (2011). A layered approach for intrusion detection using meta-modeling with classification techniques, International Journal of Computer Technology and Electronics Engineering (IJCTEE), vol 1(2), 161–168.
  • Kumaravel A, and Pradeepa R (2013). Efficient molecule reduction for drug design by intelligent search methods, International Journal of Pharama and Bio Sciences, vol 4(2): (B), 1023–1029, Available From: http://www.ijpbs.net/cms/php/upload/2348_pdf.pdf
  • Available From: http://webdocs.cs.ualberta.ca/~eisner/measures.html

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  • Layered Approach for Predicting Protein Subcellular Localization in Yeast Microarray Data

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Authors

A. Kumaravel
Department of Computer Science and Engineering, Bharath University, Selaiyur, Chennai-600073, India
R. Pradeepa
Department of Computer Science and Engineering, Bharath University, Selaiyur, Chennai - 600073

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

References





DOI: https://doi.org/10.17485/ijst%2F2013%2Fv6iS5%2F33356