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VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition.

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition.
 
Creator Saha, Sudipto
Raghava, G.P.S.
 
Subject QR Microbiology
 
Description In this study, an attempt has been made to predict the major functions of gram-negative bacterial proteins from their amino acid sequences. The dataset used for training and testing consists of 670 non-redundant gram-negative bacterial proteins (255 of cellular process, 60 of information molecules, 285 of metabolism, and 70 of virulence factors). First we developed an SVM-based method using amino acid and dipeptide composition and achieved the overall accuracy of 52.39% and 47.01%, respectively. We introduced a new concept for the classification of proteins based on tetrapeptides, in which we identified the unique tetrapeptides significantly found in a class of proteins. These tetrapeptides were used as the input feature for predicting the function of a protein and achieved the overall accuracy of 68.66%. We also developed a hybrid method in which the tetrapeptide information was used with amino acid composition and achieved the overall accuracy of 70.75%. A five-fold cross validation was used to evaluate the performance of these methods. The web server VICMpred has been developed for predicting the function of gram-negative bacterial proteins (http://www.imtech.res.in/raghava/vicmpred/).
 
Publisher Elsevier Science
 
Date 2006-02
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/151/1/raghava2006.1.pdf
Saha, Sudipto and Raghava, G.P.S. (2006) VICMpred: an SVM-based method for the prediction of functional proteins of Gram-negative bacteria using amino acid patterns and composition. Genomics, proteomics & bioinformatics / Beijing Genomics Institute, 4 (1). pp. 42-7. ISSN 1672-0229
 
Relation http://www.sciencedirect.com/science/article/pii/S1672022906600156
http://crdd.osdd.net/open/151/