Tools
 
  Alignment Analysis
  Databases
  Computational Immunology
  Modelling & 3D-structure     Analysis
  Sequence Manipulation &     Analysis
  Similarity Searches
  View all Tools
 
 Tools    >> MHCLIG
MHCLIG 
Ligand-type specificity of classical and non-classical MHCI molecules

Plea for Support
If you have found this resource useful for your research, please, tell about it to the SECRETARIA GENERAL DE CIENCIA, TECNOLOGIA E INNOVACION OF SPAIN (sgcti@mineco.es, dgi@mineco.es).Your support will be crucial to keep it up and running. Thanks.


INPUT
Replace example with your sequence/s (FASTA Format)
or Upload sequence/s from file

PREDICTION MODELS
Select the prediction models you want to use
MHCI562 Models
kNN: K-nearest neighbour algorithm 99.91 % Accuracy
SVM-RBFk: Support Vector Machine (SVM) with RBF kernel 99.77 % Accuracy
MHCI556 Models
SVM-RBFk: Support Vector Machine (SVM) with RBF kernel 100.00 % Accuracy
kNN: K-nearest neighbour algorithm 99.94 % Accuracy
SVM-Pk: SVM with Polynomial Kernel 99.46 % Accuracy
MHCI500 Models
SVM-RBFk: Support Vector Machine (SVM) with RBF kernel 100.00 % Accuracy
kNN: K-nearest neighbour algorithm 100.00 % Accuracy
SVM-Pk: SVM with Polynomial Kernel 99.42 % Accuracy
BLAST

    

Citations:
  • Martínez-Naves E, Lafuente EM & Reche PA (2011) FEBS letters 585 (21), 3478-3484

  • Martínez-Naves E, Lafuente EM & Reche PA (2011) Artificial Immune Systems, 2011, 55-65
Comments & Requests:
Hits since November/2008
Last updated:

Contact Us    |   Immunomedicine Group    |