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Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machin

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machin
 
Creator Kim, J.K.
Raghava, G.P.S.
Bang, S.
Choi, S.
 
Subject QR Microbiology
 
Description Predicting the destination of a protein in a cell is important for annotating the
function of the protein. Recent advances have allowed us to develop more accurate
methods for predicting the subcellular localization of proteins. One of the most
important factors for improving the accuracy of these methods is related to the
introduction of new useful features for protein sequences. In this paper we present
a new method for extracting appropriate features from the sequence data by com-
puting pairwise sequence alignment scores. As a classifier, support vector machine
(SVM) is used. The overall prediction accuracy evaluated by the jackknife valida-
tion technique reached 94.70% for the eukaryotic non-plant data set and 92.10% for
the eukaryotic plant data set, which is the highest prediction accuracy among the
methods reported so far with such data sets. Our experimental results confirm that
our feature extraction method based on pairwise sequence alignment is useful for
this classification problem.
 
Publisher Elsevier Science
 
Date 2006
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/1023/1/raghava2006.pdf
Kim, J.K. and Raghava, G.P.S. and Bang, S. and Choi, S. (2006) Prediction of subcellular localization of proteins using pairwise sequence alignment and support vector machin. Pattern Recognition Letters, 27. pp. 996-1001. ISSN 0167-8655
 
Relation http://crdd.osdd.net/open/1023/