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NetMHC - 4.0

NetMHC: Binding of peptides to MHC class I molecules

ANNs have been trained for 81 different Human MHC alleles including HLA-A, -B, -C and -E. Furthermore, predictions for 41 animal (Monkey, Cattle, Pig, and Mouse) alleles are available. If your molecule of interest is not found in the list below, please use NetMHCpan which can predict peptide-MHC class I binding for any allele of known sequence.

Predictions can be made for peptides of any length.
Note that most HLA molecules have a strong preference for binding 9mers. Predictions for peptides longer than 11 amino acids should be taken with caution.

NEW: View sequence motifs for the alleles in the NetMHC library at the Sequence Motifs tab below.

The project is a collaboration between CBS, ISIM, and LIAI.

Submission

Hover the mouse cursor over the symbol for a short description of the options

Type of input

Paste a single sequence or several sequences in FASTA format into the field below:

or submit a file in FASTA format directly from your local disk:


Peptide length (you may select multiple lengths):  

Select species/loci

Select Allele (max 20 per submission) or type allele names (ie HLA-A0101) separated by commas.. 

For list of allowed allele names click here List of MHC allele names.

Threshold for strong binders: % Rank  
Threshold for weak binders: % Rank  

Sort by predicted affinity  

Save output in XLS format 

Restrictions:
At most 5000 sequences per submission; each sequence not more than 20,000 amino acids and not less than 8 amino acids.

Confidentiality:
The sequences are kept confidential and will be deleted after processing.

CITATIONS AND FUNDING

Developed under the following contracts:

  * National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, contract No. HHSN272201200010C

  * Agencia Nacional de Promoción Científica y Tecnológica, Argentina (PICT-2012-0115)

For publication of results, please cite:

  • Gapped sequence alignment using artificial neural networks: application to the MHC class I system.
    Andreatta M, Nielsen M
    Bioinformatics (2016) Feb 15;32(4):511-7

    PubMed: 26515819   [PDF]

  • 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., (2003) 12:1007-17

    PubMed: 12717023

Instructions

1. Specify the input sequences

All the input sequences must be in one-letter amino acid code. The alphabet is as follows (case sensitive):

A C D E F G H I K L M N P Q R S T V W Y and X (unknown)

Any other symbol will be converted to X before processing.

The server allows for input in either FASTA or PEPTIDE format.

Sequences can be submitted in the following two formats:

  • Paste a single sequence (just the amino acids) or a number of sequences in FASTA format or a list of peptides into the upper window of the main server page.

  • Select a FASTA or PEPTIDE file on your local disk, either by typing the file name into the lower window or by browsing the disk.

At most 5000 sequences per submission; each sequence not more than 20,000 amino acids and not less than 8 amino acids.

2. Customize your run

1. Specify peptide length (only for FASTA input). By default input proteins are digested into 9-mer peptides.

2. Select species/loci from the scroll-down menu.

3. Select allele(s) from the scroll-down menu or type in the allele names separated by commas (without blank spaces). If you choose to type in the allele names, you can consult the List of MHC molecule names.; use the molecule names in the first column.

4. Optionally specify thresholds for strong and weak binders. They are expressed in terms of %Rank, that is percentile of the predicted binding affinity compared to the distribution of affinities calculated on set of 400.000 random natural peptides. The peptide will be identified as a strong binder if it is found among the top x% predicted peptides, where x% is the specified threshold for strong binders (by default 0.5%). The peptide will be identified as a weak binder if the % Rank is above the threshold of the strong binders but below the specified threshold for the weak binders (by default 2%).

5. Tick the box Sort by affinity to have the output sorted by descending predicted binding affinity.


3. Submit the job

Click on the "Submit" button. The status of your job (either 'queued' or 'running') will be displayed and constantly updated until it terminates and the server output appears in the browser window.

At any time during the wait you may enter your e-mail address and simply leave the window. Your job will continue; when it terminates you will be notified by e-mail with a URL to your results. They will be stored on the server for 24 hours.

4. Output

A description of the output format can be found in the Output format tab.

Format of NetMHC-4.0 output


DESCRIPTION


The prediction output for each molecule consists of the following columns:

  • Pos Residue number (starting from 0)

  • HLA Molecule/allele name

  • Peptide Amino acid sequence of the potential ligand

  • Core The minimal 9 amino acid binding core directly in contact with the MHC

  • Offset The starting position of the Core within the Peptide (if > 0, the method predicts a N-terminal protrusion)

  • I_pos Position of the insertion, if any.

  • I_len Length of the insertion.

  • D_pos Position of the deletion, if any.

  • D_len Length of the deletion.

  • iCore Interaction core. This is the sequence of the binding core including eventual insertions of deletions.

  • Identity Protein identifier, i.e. the name of the Fasta entry.

  • 1-log50k(aff) Log-transformed binding affinity. Some reference transformations: 50,000nM -> logAff=0; 500nM -> logAff=0.426; 50nM -> logAff=0.638; 1nM -> logAff=1.000.

  • Affinity(nM) Predicted binding affinity in nanoMolar units.

  • %Rank Rank of the predicted affinity compared to a set of 400.000 random natural peptides. This measure is not affected by inherent bias of certain molecules towards higher or lower mean predicted affinities. Strong binders are defined as having %rank<0.5, and weak binders with %rank<2. We advise to select candidate binders based on %Rank rather than nM Affinity

  • BindLevel (SB: strong binder, WB: weak binder). The peptide will be identified as a strong binder if the % Rank is below the specified threshold for the strong binders, by default 0.5%. The peptide will be identified as a weak binder if the % Rank is above the threshold of the strong binders but below the specified threshold for the weak binders, by default 2%.

    • EXAMPLE OUTPUT

    Fasta input:

    >Gag_180_209
    TPQDLNTMLNTVGGHQAAMQMLKETINEEA

    Peptide length: 8 and 9
    Allele: HLA-A*0301
    will return the following predictions: 
    

# NetMHC version 4.0

# Input is in FSA format

# Peptide length 8,9
# Affinity Threshold for Strong binding peptides  50.000
# Affinity Threshold for Weak binding peptides 500.000
# Rank Threshold for Strong binding peptides   0.500
# Rank Threshold for Weak binding peptides   2.000
-----------------------------------------------------------------------------------
  pos          HLA      peptide         Core Offset  I_pos  I_len  D_pos  D_len        iCore        Identity 1-log50k(aff) Affinity(nM)    %Rank  BindLevel
-----------------------------------------------------------------------------------
    0    HLA-A0301     TPQDLNTM    -TPQDLNTM      0      0      1      0      0     TPQDLNTM     Gag_180_209         0.014     43017.00    95.00
    1    HLA-A0301     PQDLNTML    PQDLNTML-      0      8      1      0      0     PQDLNTML     Gag_180_209         0.021     39881.02    80.00
    2    HLA-A0301     QDLNTMLN    -QDLNTMLN      0      0      1      0      0     QDLNTMLN     Gag_180_209         0.018     41073.47    85.00
    3    HLA-A0301     DLNTMLNT    DLN-TMLNT      0      3      1      0      0     DLNTMLNT     Gag_180_209         0.019     40552.86    85.00
    4    HLA-A0301     LNTMLNTV    -LNTMLNTV      0      0      1      0      0     LNTMLNTV     Gag_180_209         0.035     34098.43    55.00
    5    HLA-A0301     NTMLNTVG    NTMLNTVG-      0      8      1      0      0     NTMLNTVG     Gag_180_209         0.025     38038.41    70.00
    6    HLA-A0301     TMLNTVGG    TMLNTVGG-      0      8      1      0      0     TMLNTVGG     Gag_180_209         0.034     34544.05    55.00
    7    HLA-A0301     MLNTVGGH    MLNTV-GGH      0      5      1      0      0     MLNTVGGH     Gag_180_209         0.083     20462.88    19.00
    8    HLA-A0301     LNTVGGHQ    -LNTVGGHQ      0      0      1      0      0     LNTVGGHQ     Gag_180_209         0.018     41270.38    85.00
    9    HLA-A0301     NTVGGHQA    NTVGGHQA-      0      8      1      0      0     NTVGGHQA     Gag_180_209         0.015     42434.54    90.00
   10    HLA-A0301     TVGGHQAA    TVGGHQAA-      0      8      1      0      0     TVGGHQAA     Gag_180_209         0.021     39642.67    80.00
   11    HLA-A0301     VGGHQAAM    -VGGHQAAM      0      0      1      0      0     VGGHQAAM     Gag_180_209         0.021     39730.28    80.00
   12    HLA-A0301     GGHQAAMQ    GGHQAAMQ-      0      8      1      0      0     GGHQAAMQ     Gag_180_209         0.015     42652.28    95.00
   13    HLA-A0301     GHQAAMQM    G-HQAAMQM      0      1      1      0      0     GHQAAMQM     Gag_180_209         0.020     40135.11    80.00
   14    HLA-A0301     HQAAMQML    HQAAMQML-      0      8      1      0      0     HQAAMQML     Gag_180_209         0.057     27116.52    31.00
   15    HLA-A0301     QAAMQMLK    -QAAMQMLK      0      0      1      0      0     QAAMQMLK     Gag_180_209         0.238      3800.57     4.50
   16    HLA-A0301     AAMQMLKE    AAM-QMLKE      0      3      1      0      0     AAMQMLKE     Gag_180_209         0.021     39659.42    80.00
   17    HLA-A0301     AMQMLKET    AMQMLKET-      0      8      1      0      0     AMQMLKET     Gag_180_209         0.019     40509.00    85.00
   18    HLA-A0301     MQMLKETI    MQMLKET-I      0      7      1      0      0     MQMLKETI     Gag_180_209         0.033     35088.76    60.00
   19    HLA-A0301     QMLKETIN    QMLKETIN-      0      8      1      0      0     QMLKETIN     Gag_180_209         0.029     36469.85    65.00
   20    HLA-A0301     MLKETINE    MLKETINE-      0      8      1      0      0     MLKETINE     Gag_180_209         0.027     37444.68    70.00
   21    HLA-A0301     LKETINEE    -LKETINEE      0      0      1      0      0     LKETINEE     Gag_180_209         0.011     44465.09    99.00
   22    HLA-A0301     KETINEEA    KE-TINEEA      0      2      1      0      0     KETINEEA     Gag_180_209         0.010     44649.25    99.00
    0    HLA-A0301    TPQDLNTML    TPQDLNTML      0      0      0      0      0    TPQDLNTML     Gag_180_209         0.031     35876.13    60.00
    1    HLA-A0301    PQDLNTMLN    PQDLNTMLN      0      0      0      0      0    PQDLNTMLN     Gag_180_209         0.029     36353.23    65.00
    2    HLA-A0301    QDLNTMLNT    QDLNTMLNT      0      0      0      0      0    QDLNTMLNT     Gag_180_209         0.033     35061.82    60.00
    3    HLA-A0301    DLNTMLNTV    DLNTMLNTV      0      0      0      0      0    DLNTMLNTV     Gag_180_209         0.056     27138.82    31.00
    4    HLA-A0301    LNTMLNTVG    LNTMLNTVG      0      0      0      0      0    LNTMLNTVG     Gag_180_209         0.021     39713.52    80.00
    5    HLA-A0301    NTMLNTVGG    NTMLNTVGG      0      0      0      0      0    NTMLNTVGG     Gag_180_209         0.043     31478.50    43.00
    6    HLA-A0301    TMLNTVGGH    TMLNTVGGH      0      0      0      0      0    TMLNTVGGH     Gag_180_209         0.292      2129.03     3.00
    7    HLA-A0301    MLNTVGGHQ    MLNTVGGHQ      0      0      0      0      0    MLNTVGGHQ     Gag_180_209         0.122     13419.03    11.00
    8    HLA-A0301    LNTVGGHQA    LNTVGGHQA      0      0      0      0      0    LNTVGGHQA     Gag_180_209         0.021     39696.75    80.00
    9    HLA-A0301    NTVGGHQAA    NTVGGHQAA      0      0      0      0      0    NTVGGHQAA     Gag_180_209         0.037     33383.30    49.00
   10    HLA-A0301    TVGGHQAAM    TVGGHQAAM      0      0      0      0      0    TVGGHQAAM     Gag_180_209         0.078     21511.99    21.00
   11    HLA-A0301    VGGHQAAMQ    VGGHQAAMQ      0      0      0      0      0    VGGHQAAMQ     Gag_180_209         0.020     40406.14    80.00
   12    HLA-A0301    GGHQAAMQM    GGHQAAMQM      0      0      0      0      0    GGHQAAMQM     Gag_180_209         0.048     29872.45    38.00
   13    HLA-A0301    GHQAAMQML    GHQAAMQML      0      0      0      0      0    GHQAAMQML     Gag_180_209         0.043     31303.24    42.00
   14    HLA-A0301    HQAAMQMLK    HQAAMQMLK      0      0      0      0      0    HQAAMQMLK     Gag_180_209         0.681        31.42     0.15 <= SB
   15    HLA-A0301    QAAMQMLKE    QAAMQMLKE      0      0      0      0      0    QAAMQMLKE     Gag_180_209         0.041     32014.67    45.00
   16    HLA-A0301    AAMQMLKET    AAMQMLKET      0      0      0      0      0    AAMQMLKET     Gag_180_209         0.033     35022.77    60.00
   17    HLA-A0301    AMQMLKETI    AMQMLKETI      0      0      0      0      0    AMQMLKETI     Gag_180_209         0.057     26947.74    31.00
   18    HLA-A0301    MQMLKETIN    MQMLKETIN      0      0      0      0      0    MQMLKETIN     Gag_180_209         0.045     30830.30    41.00
   19    HLA-A0301    QMLKETINE    QMLKETINE      0      0      0      0      0    QMLKETINE     Gag_180_209         0.064     25009.20    27.00
   20    HLA-A0301    MLKETINEE    MLKETINEE      0      0      0      0      0    MLKETINEE     Gag_180_209         0.051     28662.32    35.00
   21    HLA-A0301    LKETINEEA    LKETINEEA      0      0      0      0      0    LKETINEEA     Gag_180_209         0.013     43256.44    95.00
-----------------------------------------------------------------------------------

Protein Gag_180_209. Allele HLA-A0301. Number of high binders 1. Number of weak binders 0. Number of peptides 45

Link to Allele Frequencies in Worldwide Populations HLA-A0301
-----------------------------------------------------------------------------------

    XLS output format

    The XLS output summarizes the results in a convenient tabular format which can be read by spreadsheet programs such as Excel. For each peptide submitted for prediction, the table reports:

  • the predicted affinity (nM)

  • the percentile Rank, and

  • the predicted binding Core

    for all the specified alleles.

    Two additional columns summarize the predictions across alleles. These quantitites may be useful for the design of peptides that ensure coverage across parts of the population:

  • H_Avg_Ranks harmonic mean of the %Rank calculated over all specified alleles

  • N_binders the number of alleles covered by a given peptide; in other words, how many MHC alleles the peptides is predicted to bind to.


    Example XLS output:
    XLS example output

    • References

    Main references:

    Gapped sequence alignment using artificial neural networks: application to the MHC class I system
     Massimo Andreatta1 and Morten Nielsen1,2

    Bioinformatics, Feb 15;32(4):511-7 2016

    1Instituto de Investigaciones Biotecnológicas, Universidad Nacional de San Martin, San Martín, Buenos Aires, Argentina
    2Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark

    Motivation: Many biological processes are guided by receptor interactions with linear ligands of variable length. One such receptor is the MHC class I molecule. The length preferences vary depending on the MHC allele, but are generally limited to peptides of length 8 to 11 amino acids. On this relatively simple system, we developed a sequence alignment method based on artificial neural networks that allows insertions and deletions in the alignment.
    Results: We show that prediction methods based on alignments that include insertions and deletions have significantly higher performance than methods trained on peptides of single lengths. Also, we illustrate how the location of deletions can aid the interpretation of the modes of binding of the peptide-MHC, as in the case of long peptides bulging out of the MHC groove or protruding at either terminus. Finally, we demonstrate that the method can learn the length profile of different MHC molecules, and quantified the reduction of the experimental effort required to identify potential epitopes using our prediction algorithm.
    Availability: The NetMHC-4.0 method for the prediction of peptide-MHC class I binding affinity using gapped sequence alignment is publicly available at: http://www.cbs.dtu.dk/services/NetMHC-4.0.
    Contact: mniel@cbs.dtu.dk
    Supplementary information: Supplementary data are available at Bioinformatics online.

    PMID: 26515819   [PDF]

    NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11
     Lundegaard C1Lamberth K2Harndahl M2Buus S2Lund O1Nielsen M1

    Nucleic Acids Research 36 (suppl 2): W509-W512. 2008

    1Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark
    2Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark

    NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8–11 for all 122 alleles. artificial neural network predictions are given as actual IC50 values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75–80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set.

    PMID: 18463140   (full text version available)

    Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.
     Nielsen M1, Lundegaard C1, Worning P1, Lauemoller SL2, Lamberth K2,Buus S2, Brunak S1, Lund O1

    Protein Sci., 12:1007-17, 2003.

    1Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark
    2Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark

    In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.

    PMID: 2323871   (full text version available)


    Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach.
     Buus S1, Lauemoller SL1, Worning P2, Kesmir C2, Frimurer T2, Corbet S3, Fomsgaard A3, Hilden J4, Holm A5, Brunak S2.
    Tissue Antigens., 62:378-84, 2003.

    1Division of Experimental Immunology, Institute of Medical Microbiology and Immunology, University of Copenhagen, Denmark
    2Center for Biological Sequence Analysis, Technical University of Denmark, DK-2800 Lyngby, Denmark
    3Department of Virology, State Serum Institute, Denmark
    4Department of Biostatistics, University of Copenhagen, Denmark
    5Research Center for Medical Biotechnology, Chemistry Department, Royal Veterinary and Agricultural University, Denmark

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict binding vs non-binding peptides. Furthermore, quantitative ANN allowed a straightforward application of a 'Query by Committee' (QBC) principle whereby particularly information-rich peptides could be identified and subsequently tested experimentally. Iterative training based on QBC-selected peptides considerably increased the sensitivity without compromising the efficiency of the prediction. This suggests a general, rational and unbiased approach to the development of high quality predictions of epitopes restricted to this and other HLA molecules. Due to their quantitative nature, such predictions will cover a wide range of MHC-binding affinities of immunological interest, and they can be readily integrated with predictions of other events involved in generating immunogenic epitopes. These predictions have the capacity to perform rapid proteome-wide searches for epitopes. Finally, it is an example of an iterative feedback loop whereby advanced, computational bioinformatics optimize experimental strategy, and vice versa.

    PMID: 14617044   (full text version available)


    Version history

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

    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.

    Software Downloads


    GETTING HELP

    If you need help regarding technical issues (e.g. errors or missing results) contact Technical Support. Please include the name of the service and version (e.g. NetPhos-4.0). If the error occurs after the job has started running, please include the JOB ID (the long code that you see while the job is running).

    If you have scientific questions (e.g. how the method works or how to interpret results), contact Correspondence.

    Correspondence: Technical Support: