@article{open614,
          volume = {5},
          number = {3-4},
           month = {December},
          author = {S Muthukrishnan and Aarti Garg and G.P.S. Raghava},
            note = {Copyright of this article belongs to Elsevier Science},
           title = {Oxypred: prediction and classification of oxygen-binding proteins.},
       publisher = {Elsevier Science},
            year = {2007},
         journal = {Genomics, proteomics \& bioinformatics / Beijing Genomics Institute},
           pages = {250--2},
        keywords = {oxygen-binding proteins, SVM modules, hemoglobin, web server, prediction},
             url = {http://crdd.osdd.net/open/614/},
        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/).}
}