Machine Learning Methods for Predicting HLA-Peptide Binding Activity

As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune...

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Main Authors: Heng Luo, Hao Ye, Hui Wen Ng, Lemming Shi, Weida Tong, Donna L. Mendrick, Huixiao Hong
Format: Article
Language:English
Published: SAGE Publishing 2015-01-01
Series:Bioinformatics and Biology Insights
Online Access:https://doi.org/10.4137/BBI.S29466
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author Heng Luo
Hao Ye
Hui Wen Ng
Lemming Shi
Weida Tong
Donna L. Mendrick
Huixiao Hong
author_facet Heng Luo
Hao Ye
Hui Wen Ng
Lemming Shi
Weida Tong
Donna L. Mendrick
Huixiao Hong
author_sort Heng Luo
collection DOAJ
description As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA-peptide binding prediction. We also summarize the descriptors based on which the HLA-peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA-peptide binding prediction method based on network analysis.
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spelling doaj.art-1d39c884c61a46d985e80c88b3cbb9162022-12-21T18:56:05ZengSAGE PublishingBioinformatics and Biology Insights1177-93222015-01-019s310.4137/BBI.S29466Machine Learning Methods for Predicting HLA-Peptide Binding ActivityHeng Luo0Hao Ye1Hui Wen Ng2Lemming Shi3Weida Tong4Donna L. Mendrick5Huixiao Hong6University of Arkansas at Little Rock/ University of Arkansas for Medical Sciences Bioinformatics Graduate Program, Little Rock, AR, USA.National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.Center for Pharmacogenomics, School of Pharmacy, Fudan University, Shanghai, China.National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.National Center for Toxicological Research, U.S. Food and Drug Administration, Jefferson, AR, USA.As major histocompatibility complexes in humans, the human leukocyte antigens (HLAs) have important functions to present antigen peptides onto T-cell receptors for immunological recognition and responses. Interpreting and predicting HLA-peptide binding are important to study T-cell epitopes, immune reactions, and the mechanisms of adverse drug reactions. We review different types of machine learning methods and tools that have been used for HLA-peptide binding prediction. We also summarize the descriptors based on which the HLA-peptide binding prediction models have been constructed and discuss the limitation and challenges of the current methods. Lastly, we give a future perspective on the HLA-peptide binding prediction method based on network analysis.https://doi.org/10.4137/BBI.S29466
spellingShingle Heng Luo
Hao Ye
Hui Wen Ng
Lemming Shi
Weida Tong
Donna L. Mendrick
Huixiao Hong
Machine Learning Methods for Predicting HLA-Peptide Binding Activity
Bioinformatics and Biology Insights
title Machine Learning Methods for Predicting HLA-Peptide Binding Activity
title_full Machine Learning Methods for Predicting HLA-Peptide Binding Activity
title_fullStr Machine Learning Methods for Predicting HLA-Peptide Binding Activity
title_full_unstemmed Machine Learning Methods for Predicting HLA-Peptide Binding Activity
title_short Machine Learning Methods for Predicting HLA-Peptide Binding Activity
title_sort machine learning methods for predicting hla peptide binding activity
url https://doi.org/10.4137/BBI.S29466
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