丁香实验_LOGO
登录
提问
我要登录
|免费注册
点赞
收藏
wx-share
分享

In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC

互联网

920
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 vector 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.
提问
扫一扫
丁香实验小程序二维码
实验小助手
丁香实验公众号二维码
扫码领资料
反馈
TOP
打开小程序