Our server provides two options for submitting the query sequences. The
first option user can paste their fasta sequence in the given inbox. The other option user can upload the
The dataset used in this study consists of 75 well annotated fungal adhesins and 341 non-adhesins proteins.This dataset was used to train and test our method.
We have used different compositional features as well as PSI-BLAST derived PSSM matrices to train support vector machines.
Support Vector Machine:-
Support Vector Machine Support vector machine (SVM) is a
novel machine learning method. It is based on the statistical learning
theory presented by V.N.Vapnik, it has been successfully applied to numerous
classification and pattern recognition problems such as text categorization,
image recognition and bioinformatics. The application of SVM results in the
globally optimized while with neural networks, the gradient based on training
algorithms and the solution for a classification problems. The SVM light is
a freely downloadable package written by Joachim's which can be downloadable
Evaluation of Performance:-
The accuracy of results commonly measured by the quantity of True Positives (TP), True Negatives (TN),False Positives (FP) and False Negatives (FN). In the prediction system the total prediction accuracy, Matthew's correlation coefficient(MCC), sensitivity and specificity was calculated by following equations.
Sensitivity = TP / (TP+FN),
Specificity = TN / (TN+FP),
Accuracy = TP+TN / TP+TN+FP+FN and
MCC = sqrt [(TP*TN)-(FP*FN)/(TP+FN)*(TP+FP)*(TN+FP)*(TN+FN)]