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Content Provider | IEEE Xplore Digital Library |
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Author | Sriwastava, B.K. Basu, S. Maulik, U. |
Copyright Year | 2004 |
Abstract | Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo. |
Sponsorship | IEEE Computer Society |
Starting Page | 1394 |
Ending Page | 1404 |
Page Count | 11 |
File Size | 215936 |
File Format | |
ISSN | 15455963 |
Volume Number | 12 |
Issue Number | 6 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2015-11-01 |
Publisher Place | U.S.A. |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Proteins Support vector machines Vectors Training Protein engineering Bioinformatics IEEE transactions Protein-protein interaction Pattern analysis Classifier design and evaluation Fuzzy Support Vector Machine protein-protein interaction. classifier design and evaluation fuzzy support vector machine bioinformatics |
Content Type | Text |
Resource Type | Article |
Subject | Genetics Biotechnology Applied Mathematics |
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