Enhancement to the RANKPEP resource for the prediction of peptide binding to MHC molecules using profiles

By Pedro A. Reche, John-Paul Glutting, Hong Zhang, Ellis L. Reinherz. Immunogenetics, 2004

386 citations recorded by the source corpus.

Subfields: Immunology, Molecular Biology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/8ef30a4dcb0ef1af8522d2a4ac3922457ba62a8c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Immunology

1. Morten Nielsen
2. Bjoern Peters
3. Alessandro Sette
4. Massimo Andreatta
5. Paolo Marcatili
6. John Sidney
7. J. Greenbaum
8. Zhen Yan

In Molecular Biology

1. Morten Nielsen
2. Bjoern Peters
3. Alessandro Sette
4. Massimo Andreatta
5. John Sidney
6. Paolo Marcatili
7. J. Greenbaum
8. Peng Wang

In Radiology, Nuclear Medicine and Imaging

1. Bjoern Peters
2. Alessandro Sette
3. Massimo Andreatta
4. Morten Nielsen
5. John Sidney
6. Paolo Marcatili
7. J. Greenbaum
8. Sinu Paul

Citations in the corpus

• A Systematic Assessment of MHC Class II Peptide Binding Predictions and Evaluation of a Consensus Approach (2008, methodology): “The next most comprehensive method is RANKPEP which covers 46 types, 16 of which overlap with our dataset.”
• Vaxign: The First Web-Based Vaccine Design Program for Reverse Vaccinology and Applications for Vaccine Development (2010, result): “Our results indicate that Vaxign is an accurate and efficient vaccine design program.”
• Improved methods for predicting peptide binding affinity to MHC class II molecules (2018, methodology): “Given this, many different methods have been developed, including NetMHCII (15), NetMHCIIpan (16), TEPITOPE (17), TEPITOPEpan (18),PROPRED (19), RANKPEP (20)(21) and SVRMHC (22).”