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Application of machine learning techniques in predicting MHC binders.

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Application of machine learning techniques in predicting MHC binders.
 
Creator Lata, Sneh
Bhasin, Manoj
Raghava, G.P.S.
 
Subject QH301 Biology
 
Description 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.
 
Publisher Springer Science
 
Date 2007
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/597/1/raghava07.1.pdf
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
 
Relation http://www.springerlink.com/content/v581743021u51810/fulltext.pdf
http://crdd.osdd.net/open/597/