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Improving Protein Localization Prediction Using Amino Acid Group Based Physichemical Encoding

  • Conference paper
Bioinformatics and Computational Biology (BICoB 2009)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 5462))

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Abstract

Computational prediction of protein localization is one common way to characterize the functions of newly sequenced proteins. Sequence features such as amino acid (AA) composition have been widely used for subcellular localization prediction due to their simplicity while suffering from low coverage and low prediction accuracy. We present a physichemical encoding method that maps protein sequences into feature vectors composed of the locations and lengths of amino acid groups (AAGs) with similar physichemical properties. This high-level modular representation of protein sequences overcomes the shortcoming of losing order information in the commonly used AA composition and AA pair composition encoding. When applied with SVM classifiers, we showed that AAG based features are able to achieve higher prediction accuracy (up to 20% improvement) than the widely used AA composition and AA pair composition to differentiate proteins of different localizations. When AAGs and AA composition encoding combined, the prediction accuracy can be further improved thus achieving synergistic effect.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, University of South Carolina, Columbia, SC, 29208, USA

    Jianjun Hu & Fan Zhang

Authors
  1. Jianjun Hu
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  2. Fan Zhang
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Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Connecticut, 257 ITE Building, 371 Fairfield Way, CT 06269-2155, Storrs, USA

    Sanguthevar Rajasekaran

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© 2009 Springer-Verlag Berlin Heidelberg

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Hu, J., Zhang, F. (2009). Improving Protein Localization Prediction Using Amino Acid Group Based Physichemical Encoding. In: Rajasekaran, S. (eds) Bioinformatics and Computational Biology. BICoB 2009. Lecture Notes in Computer Science(), vol 5462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00727-9_24

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  • DOI: https://doi.org/10.1007/978-3-642-00727-9_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00726-2

  • Online ISBN: 978-3-642-00727-9

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