GENERAL INFORMATION

Sequence Submission
Input Sequence:-
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 sequence files.
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.

Methodology:-
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 SVM light is a freely downloadable package written by Joachim's which can be downloadable from http://ais.gmd.de/~thorsten/svm_light/.

Artificial Neural Network:-
An artificial neural network (ANN), usually called "neural network" (NN), is a mathematical model or computational model that tries to simulate the structure and/or functional aspects of biological neural networks. It consists of an interconnected group of artificial neurons and processes information using a connectionist approach to computation. We used the freely available package SNNSv4.2 from http://www.ra.cs.uni-tuebingen.de/downloads/

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)]