Application of machine learning techniques in predicting MHC binders.

Lata, Sneh and Bhasin, Manoj and Raghava, G.P.S. (2007) Application of machine learning techniques in predicting MHC binders. Methods in molecular biology (Clifton, N.J.), 409. pp. 201-15. ISSN 1064-3745

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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.

Item Type: Article
Additional Information: 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
Subjects: Q Science > QH Natural history > QH301 Biology
QH301 Biology
Depositing User: Dr. K.P.S.Sengar
Date Deposited: 08 Dec 2011 19:39
Last Modified: 08 Dec 2011 19:39

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