MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction

Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consid...
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Autor*in:	Guo, Linyuan [verfasserIn] Luo, Cheng Zhu, Shanfeng

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Major Histocompatibility Complex Support Vector Regression Major Histocompatibility Complex Molecule Generic String Binding Prediction

Anmerkung:	© Guo et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (

Übergeordnetes Werk:	Enthalten in: BMC genomics - London : BioMed Central, 2000, 14(2013), Suppl 5 vom: 16. Okt.
Übergeordnetes Werk:	volume:14 ; year:2013 ; number:Suppl 5 ; day:16 ; month:10

Links:	Volltext

DOI / URN:	10.1186/1471-2164-14-S5-S11

Katalog-ID:	SPR027086569

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520			\|a Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html.
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700	1		\|a Luo, Cheng \|4 aut
700	1		\|a Zhu, Shanfeng \|4 aut
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allfields	10.1186/1471-2164-14-S5-S11 doi (DE-627)SPR027086569 (SPR)1471-2164-14-S5-S11-e DE-627 ger DE-627 rakwb eng Guo, Linyuan verfasserin aut MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. Major Histocompatibility Complex (dpeaa)DE-He213 Support Vector Regression (dpeaa)DE-He213 Major Histocompatibility Complex Molecule (dpeaa)DE-He213 Generic String (dpeaa)DE-He213 Binding Prediction (dpeaa)DE-He213 Luo, Cheng aut Zhu, Shanfeng aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 5 vom: 16. Okt. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 5 day:16 month:10 https://dx.doi.org/10.1186/1471-2164-14-S5-S11 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_70 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 14 2013 Suppl 5 16 10
spelling	10.1186/1471-2164-14-S5-S11 doi (DE-627)SPR027086569 (SPR)1471-2164-14-S5-S11-e DE-627 ger DE-627 rakwb eng Guo, Linyuan verfasserin aut MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. Major Histocompatibility Complex (dpeaa)DE-He213 Support Vector Regression (dpeaa)DE-He213 Major Histocompatibility Complex Molecule (dpeaa)DE-He213 Generic String (dpeaa)DE-He213 Binding Prediction (dpeaa)DE-He213 Luo, Cheng aut Zhu, Shanfeng aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 5 vom: 16. Okt. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 5 day:16 month:10 https://dx.doi.org/10.1186/1471-2164-14-S5-S11 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_70 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 14 2013 Suppl 5 16 10
allfields_unstemmed	10.1186/1471-2164-14-S5-S11 doi (DE-627)SPR027086569 (SPR)1471-2164-14-S5-S11-e DE-627 ger DE-627 rakwb eng Guo, Linyuan verfasserin aut MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. Major Histocompatibility Complex (dpeaa)DE-He213 Support Vector Regression (dpeaa)DE-He213 Major Histocompatibility Complex Molecule (dpeaa)DE-He213 Generic String (dpeaa)DE-He213 Binding Prediction (dpeaa)DE-He213 Luo, Cheng aut Zhu, Shanfeng aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 5 vom: 16. Okt. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 5 day:16 month:10 https://dx.doi.org/10.1186/1471-2164-14-S5-S11 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_70 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 14 2013 Suppl 5 16 10
allfieldsGer	10.1186/1471-2164-14-S5-S11 doi (DE-627)SPR027086569 (SPR)1471-2164-14-S5-S11-e DE-627 ger DE-627 rakwb eng Guo, Linyuan verfasserin aut MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. Major Histocompatibility Complex (dpeaa)DE-He213 Support Vector Regression (dpeaa)DE-He213 Major Histocompatibility Complex Molecule (dpeaa)DE-He213 Generic String (dpeaa)DE-He213 Binding Prediction (dpeaa)DE-He213 Luo, Cheng aut Zhu, Shanfeng aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 5 vom: 16. Okt. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 5 day:16 month:10 https://dx.doi.org/10.1186/1471-2164-14-S5-S11 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_70 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 14 2013 Suppl 5 16 10
allfieldsSound	10.1186/1471-2164-14-S5-S11 doi (DE-627)SPR027086569 (SPR)1471-2164-14-S5-S11-e DE-627 ger DE-627 rakwb eng Guo, Linyuan verfasserin aut MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Guo et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License ( Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. Major Histocompatibility Complex (dpeaa)DE-He213 Support Vector Regression (dpeaa)DE-He213 Major Histocompatibility Complex Molecule (dpeaa)DE-He213 Generic String (dpeaa)DE-He213 Binding Prediction (dpeaa)DE-He213 Luo, Cheng aut Zhu, Shanfeng aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 5 vom: 16. Okt. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 5 day:16 month:10 https://dx.doi.org/10.1186/1471-2164-14-S5-S11 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA 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_70 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_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 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 14 2013 Suppl 5 16 10
language	English
source	Enthalten in BMC genomics 14(2013), Suppl 5 vom: 16. Okt. volume:14 year:2013 number:Suppl 5 day:16 month:10
sourceStr	Enthalten in BMC genomics 14(2013), Suppl 5 vom: 16. Okt. volume:14 year:2013 number:Suppl 5 day:16 month:10
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Major Histocompatibility Complex Support Vector Regression Major Histocompatibility Complex Molecule Generic String Binding Prediction
isfreeaccess_bool	false
container_title	BMC genomics
authorswithroles_txt_mv	Guo, Linyuan @@aut@@ Luo, Cheng @@aut@@ Zhu, Shanfeng @@aut@@
publishDateDaySort_date	2013-10-16T00:00:00Z
hierarchy_top_id	326644954
id	SPR027086569
language_de	englisch
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author	Guo, Linyuan
spellingShingle	Guo, Linyuan misc Major Histocompatibility Complex misc Support Vector Regression misc Major Histocompatibility Complex Molecule misc Generic String misc Binding Prediction MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction
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topic_title	MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction Major Histocompatibility Complex (dpeaa)DE-He213 Support Vector Regression (dpeaa)DE-He213 Major Histocompatibility Complex Molecule (dpeaa)DE-He213 Generic String (dpeaa)DE-He213 Binding Prediction (dpeaa)DE-He213
topic	misc Major Histocompatibility Complex misc Support Vector Regression misc Major Histocompatibility Complex Molecule misc Generic String misc Binding Prediction
topic_unstemmed	misc Major Histocompatibility Complex misc Support Vector Regression misc Major Histocompatibility Complex Molecule misc Generic String misc Binding Prediction
topic_browse	misc Major Histocompatibility Complex misc Support Vector Regression misc Major Histocompatibility Complex Molecule misc Generic String misc Binding Prediction
format_facet	Elektronische Aufsätze Aufsätze Elektronische Ressource
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title	MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction
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title_full	MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction
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author_browse	Guo, Linyuan Luo, Cheng Zhu, Shanfeng
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doi_str_mv	10.1186/1471-2164-14-S5-S11
title_sort	mhc2skpan: a novel kernel based approach for pan-specific mhc class ii peptide binding prediction
title_auth	MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction
abstract	Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. © Guo et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstractGer	Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. © Guo et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
abstract_unstemmed	Background Computational methods for the prediction of Major Histocompatibility Complex (MHC) class II binding peptides play an important role in facilitating the understanding of immune recognition and the process of epitope discovery. To develop an effective computational method, we need to consider two important characteristics of the problem: (1) the length of binding peptides is highly flexible; and (2) MHC molecules are extremely polymorphic and for the vast majority of them there are no sufficient training data. Methods We develop a novel string kernel MHC2SK (MHC-II String Kernel) method to measure the similarities among peptides with variable lengths. By considering the distinct features of MHC-II peptide binding prediction problem, MHC2SK differs significantly from the recently developed kernel based method, GS (Generic String) kernel, in the way of computing similarities. Furthermore, we extend MHC2SK to MHC2SKpan for pan-specific MHC-II peptide binding prediction by leveraging the binding data of various MHC molecules. Results MHC2SK outperformed GS in allele specific prediction using a benchmark dataset, which demonstrates the effectiveness of MHC2SK. Furthermore, we evaluated the performance of MHC2SKpan using various benckmark data sets from several different perspectives: Leave-one-allele-out (LOO), 5-fold cross validation as well as independent data testing. MHC2SKpan has achieved comparable performance with NetMHCIIpan-2.0 and outperformed NetMHCIIpan-1.0, TEPITOPEpan and MultiRTA, being statistically significant. MHC2SKpan can be freely accessed at http://datamining-iip.fudan.edu.cn/service/MHC2SKpan/index.html. © Guo et al.; licensee BioMed Central Ltd. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (
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title_short	MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction
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MHC2SKpan: a novel kernel based approach for pan-specific MHC class II peptide binding prediction

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