Enterobacteria virulence factor prediction server

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.