標題: Prediction of the bonding states of cysteines using the support vector machines based on multiple feature vectors and cysteine state sequences
作者: Chen, YC
Lin, SC
Lin, CJ
Hwang, JK
生物科技學系
生物資訊及系統生物研究所
Department of Biological Science and Technology
Institude of Bioinformatics and Systems Biology
關鍵字: support vector machines;disulfide bonds;cysteine state sequences;multiple feature vectors
公開日期: 1-六月-2004
摘要: The support vector machine (SVM) method is used to predict the bonding states of cysteines. Besides using local descriptors such as the local sequences, we include global information, such as amino acid compositions and the patterns of the states of cysteines (bonded or nonbonded), or cysteine state sequences, of the proteins. We found that SVM based on local sequences or global amino acid compositions yielded similar prediction accuracies for the data set comprising 4136 cysteine-containing segments extracted from 969 nonhomologous proteins. However, the SVM method based on multiple feature vectors (combining local sequences and global amino acid compositions) significantly improves the prediction accuracy, from 80% to 86%. If coupled with cysteine state sequences, SVM based on multiple feature vectors yields 90% in overall prediction accuracy and a 0.77 Matthews correlation coefficient, around 10% and 22% higher than the corresponding values obtained by SVM based on local sequence information. (C) 2004Wiley-Liss, Inc.
URI: http://dx.doi.org/10.1002/prot.20079
http://hdl.handle.net/11536/26696
ISSN: 0887-3585
DOI: 10.1002/prot.20079
期刊: PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS
Volume: 55
Issue: 4
起始頁: 1036
結束頁: 1042
顯示於類別:期刊論文


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