DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity

Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to pre...
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Gespeichert in:

Autor*in:	Jingcheng Wu [verfasserIn] Wenzhe Wang [verfasserIn] Jiucheng Zhang [verfasserIn] Binbin Zhou [verfasserIn] Wenyi Zhao [verfasserIn] Zhixi Su [verfasserIn] Xun Gu [verfasserIn] Jian Wu [verfasserIn] Zhan Zhou [verfasserIn] Shuqing Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	deep learning neoantigen recurrent neural network human leukocyte antigen cancer immunology

Übergeordnetes Werk:	In: Frontiers in Immunology - Frontiers Media S.A., 2011, 10(2019)
Übergeordnetes Werk:	volume:10 ; year:2019

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fimmu.2019.02559

Katalog-ID:	DOAJ001324373

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520			\|a Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.
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spelling	10.3389/fimmu.2019.02559 doi (DE-627)DOAJ001324373 (DE-599)DOAJee869000866b404a998ca3cf4d2251ca DE-627 ger DE-627 rakwb eng RC581-607 Jingcheng Wu verfasserin aut DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan. deep learning neoantigen recurrent neural network human leukocyte antigen cancer immunology Immunologic diseases. Allergy Jingcheng Wu verfasserin aut Wenzhe Wang verfasserin aut Jiucheng Zhang verfasserin aut Binbin Zhou verfasserin aut Wenyi Zhao verfasserin aut Wenyi Zhao verfasserin aut Zhixi Su verfasserin aut Xun Gu verfasserin aut Jian Wu verfasserin aut Zhan Zhou verfasserin aut Shuqing Chen verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 10(2019) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:10 year:2019 https://doi.org/10.3389/fimmu.2019.02559 kostenfrei https://doaj.org/article/ee869000866b404a998ca3cf4d2251ca kostenfrei https://www.frontiersin.org/article/10.3389/fimmu.2019.02559/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019
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allfieldsSound	10.3389/fimmu.2019.02559 doi (DE-627)DOAJ001324373 (DE-599)DOAJee869000866b404a998ca3cf4d2251ca DE-627 ger DE-627 rakwb eng RC581-607 Jingcheng Wu verfasserin aut DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan. deep learning neoantigen recurrent neural network human leukocyte antigen cancer immunology Immunologic diseases. Allergy Jingcheng Wu verfasserin aut Wenzhe Wang verfasserin aut Jiucheng Zhang verfasserin aut Binbin Zhou verfasserin aut Wenyi Zhao verfasserin aut Wenyi Zhao verfasserin aut Zhixi Su verfasserin aut Xun Gu verfasserin aut Jian Wu verfasserin aut Zhan Zhou verfasserin aut Shuqing Chen verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 10(2019) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:10 year:2019 https://doi.org/10.3389/fimmu.2019.02559 kostenfrei https://doaj.org/article/ee869000866b404a998ca3cf4d2251ca kostenfrei https://www.frontiersin.org/article/10.3389/fimmu.2019.02559/full kostenfrei https://doaj.org/toc/1664-3224 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2019
language	English
source	In Frontiers in Immunology 10(2019) volume:10 year:2019
sourceStr	In Frontiers in Immunology 10(2019) volume:10 year:2019
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	deep learning neoantigen recurrent neural network human leukocyte antigen cancer immunology Immunologic diseases. Allergy
isfreeaccess_bool	true
container_title	Frontiers in Immunology
authorswithroles_txt_mv	Jingcheng Wu @@aut@@ Wenzhe Wang @@aut@@ Jiucheng Zhang @@aut@@ Binbin Zhou @@aut@@ Wenyi Zhao @@aut@@ Zhixi Su @@aut@@ Xun Gu @@aut@@ Jian Wu @@aut@@ Zhan Zhou @@aut@@ Shuqing Chen @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
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callnumber-first	R - Medicine
author	Jingcheng Wu
spellingShingle	Jingcheng Wu misc RC581-607 misc deep learning misc neoantigen misc recurrent neural network misc human leukocyte antigen misc cancer immunology misc Immunologic diseases. Allergy DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity
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topic_title	RC581-607 DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity deep learning neoantigen recurrent neural network human leukocyte antigen cancer immunology
topic	misc RC581-607 misc deep learning misc neoantigen misc recurrent neural network misc human leukocyte antigen misc cancer immunology misc Immunologic diseases. Allergy
topic_unstemmed	misc RC581-607 misc deep learning misc neoantigen misc recurrent neural network misc human leukocyte antigen misc cancer immunology misc Immunologic diseases. Allergy
topic_browse	misc RC581-607 misc deep learning misc neoantigen misc recurrent neural network misc human leukocyte antigen misc cancer immunology misc Immunologic diseases. Allergy
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title	DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity
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title_full	DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity
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title_sort	deephlapan: a deep learning approach for neoantigen prediction considering both hla-peptide binding and immunogenicity
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title_auth	DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity
abstract	Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.
abstractGer	Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.
abstract_unstemmed	Neoantigens play important roles in cancer immunotherapy. Current methods used for neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and peptides, which is insufficient for high-confidence neoantigen prediction. In this study, we apply deep learning techniques to predict neoantigens considering both the possibility of HLA-peptide binding (binding model) and the potential immunogenicity (immunogenicity model) of the peptide-HLA complex (pHLA). The binding model achieves comparable performance with other well-acknowledged tools on the latest Immune Epitope Database (IEDB) benchmark datasets and an independent mass spectrometry (MS) dataset. The immunogenicity model could significantly improve the prediction precision of neoantigens. The further application of our method to the mutations with pre-existing T-cell responses indicating its feasibility in clinical application. DeepHLApan is freely available at https://github.com/jiujiezz/deephlapan and http://biopharm.zju.edu.cn/deephlapan.
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title_short	DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity
url	https://doi.org/10.3389/fimmu.2019.02559 https://doaj.org/article/ee869000866b404a998ca3cf4d2251ca https://www.frontiersin.org/article/10.3389/fimmu.2019.02559/full https://doaj.org/toc/1664-3224
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author2	Jingcheng Wu Wenzhe Wang Jiucheng Zhang Binbin Zhou Wenyi Zhao Zhixi Su Xun Gu Jian Wu Zhan Zhou Shuqing Chen
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up_date	2024-07-03T19:44:56.110Z
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DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity

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