TY  - JOUR
N1  - Methods in Molecular Biology, vol. 409: Immunoinformatics: Predicting Immunogenicity In Silico
Edited by: D. R. Flower © Humana Press Inc., Totowa, NJ
Copyright of this article belongs to Springer Science
ID  - open597
UR  - http://www.springerlink.com/content/v581743021u51810/fulltext.pdf
A1  - Lata, Sneh
A1  - Bhasin, Manoj
A1  - Raghava, G.P.S.
Y1  - 2007///
N2  - The machine learning techniques are playing a vital role in the field of immunoinformatics. In the past, a number of methods have been developed for predicting major histocompatibility complex (MHC)-binding peptides using machine learning techniques. These methods allow predicting MHC-binding peptides with high accuracy. In this chapter, we describe two machine learning technique-based methods, nHLAPred and MHC2Pred, developed for predicting MHC binders for class I and class II alleles, respectively. nHLAPred is a web server developed for predicting binders for 67 MHC class I alleles. This sever has two methods: ANNPred and ComPred. ComPred allows predicting binders for 67 MHC class I alleles, using the combined method [artificial neural network (ANN) and quantitative matrix] for 30 alleles and quantitative matrix-based method for 37 alleles. ANNPred allows prediction of binders for only 30 alleles purely based on the ANN. MHC2Pred is a support vector machine (SVM)-based method for prediction of promiscuous binders for 42 MHC class II alleles.
PB  - Springer Science
JF  - Methods in molecular biology (Clifton, N.J.)
VL  - 409
SN  - 1064-3745
TI  - Application of machine learning techniques in predicting MHC binders.
SP  - 201
AV  - restricted
EP  - 15
ER  -