Muthukrishnan, S and Garg, Aarti and Raghava, G.P.S. (2007) Oxypred: prediction and classification of oxygen-binding proteins. Genomics, proteomics & bioinformatics / Beijing Genomics Institute, 5 (3-4). pp. 250-2. ISSN 1672-0229
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Abstract
This study describes a method for predicting and classifying oxygen-binding proteins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding proteins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Secondly, an SVM module was developed based on amino acid composition, classifying the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemocyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins (available from http://www.imtech.res.in/raghava/oxypred/).
| Item Type: | Article |
|---|---|
| Additional Information: | Copyright of this article belongs to Elsevier Science |
| Uncontrolled Keywords: | oxygen-binding proteins, SVM modules, hemoglobin, web server, prediction |
| Subjects: | Q Science > QH Natural history > QH301 Biology QH301 Biology |
| Depositing User: | Dr. K.P.S.Sengar |
| Date Deposited: | 30 Nov 2011 09:45 |
| Last Modified: | 13 Dec 2011 17:18 |
| URI: | http://crdd.osdd.net/open/id/eprint/614 |
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