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
creator: 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
type: PeerReviewed
format: application/pdf
identifier: http://crdd.osdd.net/open/151/1/raghava2006.1.pdf
relation: http://www.sciencedirect.com/science/article/pii/S1672022906600156
identifier:   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://crdd.osdd.net/open/151/