Training of neural networks Morten Nielsen (email@example.com)
During this exercise you will use the EasyPred web server to
train and evaluate an artificial neural network method for
prediction of peptide MHC binding Background: Peptide MHC
binding The most selective step in identifying potential peptide
immunogens is the binding of the peptide to the MHC complex.
Only one in about 200 peptides will bind to a given MHC complex.
A very large number of different MHC alleles exist each with a
highly selective peptide The binding motif for a given MHC class
I complex is in most cases 9 amino acids long. The motif is
characterized by a strong amino acid preference at specific
positions in the motif. These position are called anchor
positions. For many MHC complexes the anchor position are placed
at P2 and P9 in the motif. However this is not always the case.
Large number of peptide data exist describing this MHC
specificity variation. One important source of data is the
SYFPEITHI MHC database (http://www.syfpeithi