Enterobacteria virulence factor prediction server
Keywords:Virulence prediction, amino acid composition, data mining, classifiers, SMO.
The continuous usage of antibiotics has resulted in the increase of multidrug resistance in the bacteria. The drastic increase in the bacterial genome projects has paved a path for the identification of potentially novel virulence-associated factors and their possibility as novel drug targets. Thus in the present study, we have implemented SMO classifiers for the better prediction of proteins based on individual protein sequences amino acid composition (AAC) and the performance of evaluation was checked via threshold dependent parameters such as Sensitivity, Specificity, Accuracy, and Mathew correlation coefficient. The predictions are based on the dataset incorporated in the database of five major virulence factors from six pathogens of Enterobacteriacae. This comprehensive database can serve as a source for the selection of significant virulence factor based on the intellectual Gene ontology terms that play a critical role in the pathogenesis and its surveillance in the host.
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