BhairPred – Prediction of beta Hairpins in Proteins using ANN and SVM Techniques

BhairPred

:: DESCRIPTION

BhairPred Predicts beta hairpins in proteins using ANN and SVM techniques. In this method secondary structure and multiple sequence alignment are used to predict the beta hairpins

::DEVELOPER

BhairPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2005 Jul 1;33(Web Server issue):W154-9.
BhairPred: prediction of beta-hairpins in a protein from multiple alignment information using ANN and SVM techniques.
Kumar M, Bhasin M, Natt NK, Raghava GP.

CTLPred – SVM & ANN Based CTL epitope Prediction method

CTLPred

:: DESCRIPTION

CTLPred is a direct method for prediction of CTL epitopes crucial in subunit vaccine design.In direct methods the information or patterns of T cell epitopes instead of MHC binders were used for the development o f methods. The methods is based on elegant machine learning techniques like a Artificial Neural network and support vector machine .

::DEVELOPER

CTLPred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Bhasin,M. and Raghava, G.P.S. (2004)
Prediction of CTL epitopes using QM, SVM and ANN techniques.
Vaccine,22,3195-3201.

HLADR4Pred – SVM and ANN based HLA-DR Bininding Peptide Prediction

HLADR4Pred

:: DESCRIPTION

The HLA-DR4Pred is an SVM and ANN based HLA-DRB1*0401(MHC class II alleles) binding peptides prediction method. The accuracy of the SVM and ANN based methods is ~86% and ~78% respectively.The performence of the methods was tested through 5 set cross-validation. The training of SVM and ANN was done by using the freely availaible SVM_LIGHT and SNNS packages respectively. The data for training of neural network and SVM model has been extracted from MHCBN database.This method will be useful in cellular immunology, Vaccine design, immunodiagnostics, immunotherapeuatics and molecular understanding of autoimmune susceptibility.

::DEVELOPER

HLADR4Pred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Web Browser

:: DOWNLOAD

:: MORE INFORMATION

Citation

Bhasin M, Raghava GPS (2004).
SVM based method for predicting HLA-DRB1 binding peptides in an antigen sequence.
Bioinformatics 20(3): 421-3

Tool for Biomedical Image Processing & Detection of Cancer using ANN

Biomedical Image Processing & Detection of Cancer

:: DESCRIPTION

An automatic tool for prediction and classification of cancerous/non-cancerous squamosal cells using image processing and artificial neural networks (ANN). The ANN program is much more flexible and user friendly. It also optimizes number of hidden nodes itself based on the prediction accuracy. The neural network used here is a two layer neural networks and it uses standard back propagation algorithm. The first layer is use for detection of nucleus, cytoplasm and background of the image.Whereas the second layer is used to classify images into cancerous or non-cancerous based on three cellular features: size of nucleus, size of cytoplasm and ratio of nucleus/cytoplasm sizes. Using image processing techniques we extracted 15 features for selected pixel (using a mouse event program written in DOT NET framework) of an image over its 3×3 neighbouring matrix (using program written in MATLAB and C). These parameters are input to first layer of ANN.

::DEVELOPER

Project Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Windows
Matlab

:: DOWNLOAD

Project

:: MORE INFORMATION