Events News Research CBS CBS Publications Bioinformatics
Staff Contact About Internal CBS CBS Other

Version history


Please click on the version number to activate the corresponding server.

3.2 The current server. New in this version:
  • Addition of Artificial Neural Network predictors for several additional alleles.
  • Average of approximation and directly trained 10mer predictions were applicable.
  • Removal of matrix predictions (Obsolete! Use NetMHCpan).

Main publication:

  • Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.
    Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O.
    Protein Sci., 12:1007-17, 2003.

    View the abstract.

  • Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers.
    Lundegaard C, Lund O, Nielsen M.
    Bioinformatics, 24(11):1397-98, 2008.

    View the abstract.

  • NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11 Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M.
    Nucleic Acids Res. 1;36(Web Server issue):W509-12. 2008

    View the abstract.

3.0 New in this version:
  • Addition of Artificial Neural Network predictors for several additional alleles.
  • Option for 8-, 10-, and 11-mer predictions
  • Additional linked output in tab-seperated text format for open in spreadsheets

Main publications:

  • Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.
    Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O.
    Protein Sci., 12:1007-17, 2003.

    View the abstract.

  • Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers.
    Lundegaard C, Lund O, Nielsen M.
    Bioinformatics, 24(11):1397-98, 2008.

    View the abstract.

  • NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11 Lundegaard C, Lamberth K, Harndahl M, Buus S, Lund O, Nielsen M.
    Nucleic Acids Res. 1;36(Web Server issue):W509-12. 2008

    View the abstract.

2.1 New in this version:
  • Addition of Artificial Neural Network predictors for several additional alleles.
  • Selection of optimal predictors for alleles where both Neural networks and Matrix predictors exists.
    • Removal of Matrix predictors for alleles for which Neural Network predictors exist.
  • Update of Web interface.
  • Indication of Strong Binder/Weak Binder on output.

Main publication:

  • Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.
    Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O.
    Protein Sci., 12:1007-17, 2003.

    View the abstract.

2.0 New in this version:
  • Improved neural network predictors for alleles belonging to most HLA supertypes.
    • Artificial Neural Network ensembles trained using several sequence encoding schemes and optimized training strategy.
  • Matrix predictors derived using a Gibbs sampler approach for a large number of alleles are introduced.

Main publication:

  • Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.
    Nielsen M, Lundegaard C, Worning P, Lauemoller SL, Lamberth K, Buus S, Brunak S, Lund O.
    Protein Sci., 12:1007-17, 2003.

    View the abstract.

  • Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach.
    Nielsen M, Lundegaard C, Worning P, Hvid CS, Lamberth K, Buus S, Brunak S, Lund O.
    Bioinformatics, 20(9):1388-97, 2004.

    View the abstract.

1.0 Original version:
  • Artificial Neural Network predictors for peptide/MHC binding for HLA-A2 and H-2Kk.
  • Integrate predictions of MHC binding and proteasomale cleavage using NetChop version 2.0

Main publication:

  • Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach.
    Buus S, Lauemoller SL, Worning P, Kesmir C, Frimurer T, Corbet S, Fomsgaard A, Hilden J, Holm A, Brunak S.
    Tissue Antigens., 62:378-84, 2003.

    View the abstract.




GETTING HELP

Scientific problems:        Technical problems: