Tag: SVM

SRTpred – SVM based method for Prediction of Secrteory Proteins

SRTpred

:: DESCRIPTION

SRTpred classifies protein sequence as secretory or non-secretory proteins. It consists of different SVM modules based on different features of proteins such as compositions(Amino acid, physicochemical prperties, and dipeptide). In addition PSI-BLAST was also used to carry out similarity-based search. Finally a hybrid approach based SVM module was developed that encapsulates complete information of a protein sequence that is amino acid and dipeptide composiiton and PSI-BLAST. This module can classify the protein sequence between secretory and non-secrtory protein with an accuracy of 83%. Users have a choice to use any of these module for predcition of their query sequence.

::DEVELOPER

SRTpred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Garg, A. and Raghava, G. P. S. (2008)
A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search.
In Silico Biology 8:129-140.

PSLpred – SVM based method for the Subcellular Localization of Prokaryotic Proteins

PSLpred

:: DESCRIPTION

 PSLpred is a method for subcellular localization proteins belongs to prokaryotic genomes. The pathogen play an important role in our life.

::DEVELOPER

PSLpred Team

: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin, M., Garg, A. and Raghava, GPS (2005)
PSLpred: prediction of subcellular localization of bacterial proteins.
Bioinformatics 21(10):2522-4.

HSLpred – SVM based method for Teh Subcellular Localization of Human Proteins

HSLpred

:: DESCRIPTION

HSLpred allows predicting the subcellulare localization of human proteins. This is based on various type of residue composition of proteins using SVM technique

::DEVELOPER

HSLpred team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Aarti Garg, Manoj Bhasin, and Gajendra P. S. Raghava (2005).
SVM-based method for subcellular localization of human proteins using amino acid compositions, their order and similarity search.
J. Biol. Chem. 280:14427-32.

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

  NO

:: 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.

Pcleavage – SVM based 20S Proteasomal Prediction Method

Pcleavage

:: DESCRIPTION

 Pcleavage is a support vector machine based method for the prediction of constitutive as well as immunoproteasome cleavage sites in antigenic sequences.

::DEVELOPER

Pcleavage Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin M & Raghava GP. (2005).
Pcleavage: an SVM based method for prediction of constitutive proteasome and immunoproteasome cleavage sites in antigenic sequences.
Nucleic Acids Res. 33: W202-7

MHC2Pred – SVM based method for MHC class II Binders Prediction

MHC2Pred

:: DESCRIPTION

The MHC2Pred is an SVM based method for prediction of promiscuous MHC class II binding peptides.The average accuracy of SVM based method for 42 alleles is ~80%. The performence of the method was poorer for few allele due to smaller size of dataset. The performence of the method was tested through 5-fold cross-validation.

::DEVELOPER

MHC2Pred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Methods Mol Biol. 2007;409:201-15.
Application of machine learning techniques in predicting MHC binders.
Lata S, Bhasin M, Raghava GP.

GPCRSclass – SVM based Classification of Amine Type of GPCR

GPCRSclass

:: DESCRIPTION

 GPCRSclass is a dipeptide composition based method for predicting Amine Type of G-protein-coupled receptors. In this method type amine is predicted from dipeptide composition of proteins using SVM.

::DEVELOPER

 GPCRSclass Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin M, Raghava GPS.
GPCRsclass: a web tool for the classification of amine type of G-protein-coupled receptors.
Nucleic Acids Res.(2005). 33(Web Server issue):W143-7

NRpred – SVM based method for Prediction of Nuclear Receptors

NRpred

:: DESCRIPTION

NRpred is a SVM based tool for the classification of nuclear receptors on the basis of amino acid composition or dipeptide composition. The overall prediction accuracy of amino acid composition and dipeptide composition based methods is 82.6% and 97.2%

::DEVELOPER

NRpred Team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

  NO

:: MORE INFORMATION

Citation

Bhasin,M. and Raghava, G.P.S. (2004)
Classification of nuclear receptors based on amino acid composition and dipeptide composition.
J Biol Chem. 279(22):23262-6

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

  NO

:: 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

TBpred – SVM based Subcellular Localization Prediction method for Mycobacterial Proteins

TBpred

:: DESCRIPTION

TBpred is a prediction server that predicts four subcellular localization (cytoplasmic,integral membrane,secretory and membrane attached by lipid anchor) of mycobacterial proteins.

::DEVELOPER

TBpred team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Web Browser

:: DOWNLOAD

 NO

:: MORE INFORMATION

Citation

BMC Bioinformatics. 2007 Sep 13;8:337.
Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs.
Rashid M1, Saha S, Raghava GP.