Predicting Protein-Peptide Binding Affinity by Learning Peptide-Peptide Distance Functions
- 1 January 2005
- book chapter
- Published by Springer Nature
- p. 456-471
- https://doi.org/10.1007/11415770_34
Abstract
No abstract availableKeywords
This publication has 15 references indexed in Scilit:
- Learning distance functions for image retrievalPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Boosting margin based distance functions for clusteringPublished by Association for Computing Machinery (ACM) ,2004
- Towards in silico prediction of immunogenic epitopesTrends in Immunology, 2003
- Sensitive quantitative predictions of peptide‐MHC binding by a ‘Query by Committee’ artificial neural network approachTissue Antigens, 2003
- Prediction of MHC class I binding peptides, using SVMHCBMC Bioinformatics, 2002
- Poor correspondence between predicted and experimental binding of peptides to class I MHC moleculesTissue Antigens, 2000
- Structure‐based prediction of binding peptides to MHC class I molecules: Application to a broad range of MHC allelesProtein Science, 2000
- Human tumor antigens for cancer vaccine developmentImmunological Reviews, 1999
- Predicting peptides that bind to MHC molecules using supervised learning of hidden markov modelsProteins-Structure Function and Bioinformatics, 1998
- MHC ligands and peptide motifs: first listingImmunogenetics, 1995