Bioinformatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Protein cellular localization prediction with Support Vector Machines and Decision Trees
Computers in Biology and Medicine
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
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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.