Abstract
| Original language | English |
|---|---|
| Title of host publication | Immunoinformatics |
| Subtitle of host publication | predicting Immunogenicity in silico |
| Editors | Darren R. Flowers |
| Place of Publication | Totowa, NJ (US) |
| Publisher | Humana Press |
| Pages | 283-291 |
| Number of pages | 9 |
| ISBN (Electronic) | 978-1-60327-118-9 |
| ISBN (Print) | 978-1-58829-699-3 |
| DOIs | |
| Publication status | Published - 16 Jul 2007 |
Publication series
| Name | Methods in molecular biology™ |
|---|---|
| Publisher | Humana Press |
| Volume | 409 |
| ISSN (Print) | 1064-3745 |
| ISSN (Electronic) | 1940-6029 |
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Keywords
- bioinformatics
- cell biology
- human genetics
- immunology
- life sciences
Cite this
}
In silico prediction of peptide-MHC binding affinity using SVRMHC. / Liu, Wen; Wan, Ji; Meng, Xiangshan; Flower, Darren R.; Li, Tongbin.
Immunoinformatics: predicting Immunogenicity in silico. ed. / Darren R. Flowers. Totowa, NJ (US) : Humana Press, 2007. p. 283-291 (Methods in molecular biology™; Vol. 409).Research output: Chapter in Book/Report/Conference proceeding › Chapter (peer-reviewed)
TY - CHAP
T1 - In silico prediction of peptide-MHC binding affinity using SVRMHC
AU - Liu, Wen
AU - Wan, Ji
AU - Meng, Xiangshan
AU - Flower, Darren R.
AU - Li, Tongbin
PY - 2007/7/16
Y1 - 2007/7/16
N2 - The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.
AB - The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide-MHC binding affinities based on support rector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide-MHC binding.
KW - bioinformatics
KW - cell biology
KW - human genetics
KW - immunology
KW - life sciences
UR - http://link.springer.com/protocol/10.1007/978-1-60327-118-9_20
U2 - 10.1007/978-1-60327-118-9_20
DO - 10.1007/978-1-60327-118-9_20
M3 - Chapter (peer-reviewed)
C2 - 18450008
SN - 978-1-58829-699-3
T3 - Methods in molecular biology™
SP - 283
EP - 291
BT - Immunoinformatics
A2 - Flowers, Darren R.
PB - Humana Press
CY - Totowa, NJ (US)
ER -