Version history


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

4.0 The current server. New in this version:
  • Improved algorithm implementing insertions and deletions in the alignment.
  • Sequence logos for all alleles in the NetMHC library.
  • New output format including Rank scores.
  • Addition of predictors for several additional alleles.

Publication:

  • Gapped sequence alignment using artificial neural networks: application to the MHC class I system.
    Andreatta M, Nielsen M.
    Bioinformatics (2015) - In press

3.4 New in this version:
  • Retrained on an extented data set covering more than 140 MHC molecules

3.2 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).

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

Publications:

  • 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.
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.

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.

  • 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

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.




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