All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning

Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capabili...
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Autor*in:	Airola, Antti [verfasserIn] Pyysalo, Sampo Björne, Jari Pahikkala, Tapio Ginter, Filip Salakoski, Tapio

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2008

Schlagwörter:	Dependency Graph Training Corpus Percentage Unit Graph Kernel Tree Kernel

Anmerkung:	© Airola et al; licensee BioMed Central Ltd. 2008

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 9(2008), Suppl 11 vom: 19. Nov.
Übergeordnetes Werk:	volume:9 ; year:2008 ; number:Suppl 11 ; day:19 ; month:11

Links:	Volltext

DOI / URN:	10.1186/1471-2105-9-S11-S2

Katalog-ID:	SPR026848244

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520			\|a Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided.
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allfields	10.1186/1471-2105-9-S11-S2 doi (DE-627)SPR026848244 (SPR)1471-2105-9-S11-S2-e DE-627 ger DE-627 rakwb eng Airola, Antti verfasserin aut All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Airola et al; licensee BioMed Central Ltd. 2008 Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. Dependency Graph (dpeaa)DE-He213 Training Corpus (dpeaa)DE-He213 Percentage Unit (dpeaa)DE-He213 Graph Kernel (dpeaa)DE-He213 Tree Kernel (dpeaa)DE-He213 Pyysalo, Sampo aut Björne, Jari aut Pahikkala, Tapio aut Ginter, Filip aut Salakoski, Tapio aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 11 vom: 19. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 11 day:19 month:11 https://dx.doi.org/10.1186/1471-2105-9-S11-S2 kostenfrei 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 Suppl 11 19 11
spelling	10.1186/1471-2105-9-S11-S2 doi (DE-627)SPR026848244 (SPR)1471-2105-9-S11-S2-e DE-627 ger DE-627 rakwb eng Airola, Antti verfasserin aut All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Airola et al; licensee BioMed Central Ltd. 2008 Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. Dependency Graph (dpeaa)DE-He213 Training Corpus (dpeaa)DE-He213 Percentage Unit (dpeaa)DE-He213 Graph Kernel (dpeaa)DE-He213 Tree Kernel (dpeaa)DE-He213 Pyysalo, Sampo aut Björne, Jari aut Pahikkala, Tapio aut Ginter, Filip aut Salakoski, Tapio aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 11 vom: 19. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 11 day:19 month:11 https://dx.doi.org/10.1186/1471-2105-9-S11-S2 kostenfrei 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 Suppl 11 19 11
allfields_unstemmed	10.1186/1471-2105-9-S11-S2 doi (DE-627)SPR026848244 (SPR)1471-2105-9-S11-S2-e DE-627 ger DE-627 rakwb eng Airola, Antti verfasserin aut All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Airola et al; licensee BioMed Central Ltd. 2008 Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. Dependency Graph (dpeaa)DE-He213 Training Corpus (dpeaa)DE-He213 Percentage Unit (dpeaa)DE-He213 Graph Kernel (dpeaa)DE-He213 Tree Kernel (dpeaa)DE-He213 Pyysalo, Sampo aut Björne, Jari aut Pahikkala, Tapio aut Ginter, Filip aut Salakoski, Tapio aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 11 vom: 19. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 11 day:19 month:11 https://dx.doi.org/10.1186/1471-2105-9-S11-S2 kostenfrei 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 Suppl 11 19 11
allfieldsGer	10.1186/1471-2105-9-S11-S2 doi (DE-627)SPR026848244 (SPR)1471-2105-9-S11-S2-e DE-627 ger DE-627 rakwb eng Airola, Antti verfasserin aut All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Airola et al; licensee BioMed Central Ltd. 2008 Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. Dependency Graph (dpeaa)DE-He213 Training Corpus (dpeaa)DE-He213 Percentage Unit (dpeaa)DE-He213 Graph Kernel (dpeaa)DE-He213 Tree Kernel (dpeaa)DE-He213 Pyysalo, Sampo aut Björne, Jari aut Pahikkala, Tapio aut Ginter, Filip aut Salakoski, Tapio aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 11 vom: 19. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 11 day:19 month:11 https://dx.doi.org/10.1186/1471-2105-9-S11-S2 kostenfrei 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 Suppl 11 19 11
allfieldsSound	10.1186/1471-2105-9-S11-S2 doi (DE-627)SPR026848244 (SPR)1471-2105-9-S11-S2-e DE-627 ger DE-627 rakwb eng Airola, Antti verfasserin aut All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning 2008 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Airola et al; licensee BioMed Central Ltd. 2008 Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. Dependency Graph (dpeaa)DE-He213 Training Corpus (dpeaa)DE-He213 Percentage Unit (dpeaa)DE-He213 Graph Kernel (dpeaa)DE-He213 Tree Kernel (dpeaa)DE-He213 Pyysalo, Sampo aut Björne, Jari aut Pahikkala, Tapio aut Ginter, Filip aut Salakoski, Tapio aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 9(2008), Suppl 11 vom: 19. Nov. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:9 year:2008 number:Suppl 11 day:19 month:11 https://dx.doi.org/10.1186/1471-2105-9-S11-S2 kostenfrei 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 9 2008 Suppl 11 19 11
language	English
source	Enthalten in BMC bioinformatics 9(2008), Suppl 11 vom: 19. Nov. volume:9 year:2008 number:Suppl 11 day:19 month:11
sourceStr	Enthalten in BMC bioinformatics 9(2008), Suppl 11 vom: 19. Nov. volume:9 year:2008 number:Suppl 11 day:19 month:11
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Dependency Graph Training Corpus Percentage Unit Graph Kernel Tree Kernel
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Airola, Antti @@aut@@ Pyysalo, Sampo @@aut@@ Björne, Jari @@aut@@ Pahikkala, Tapio @@aut@@ Ginter, Filip @@aut@@ Salakoski, Tapio @@aut@@
publishDateDaySort_date	2008-11-19T00:00:00Z
hierarchy_top_id	326644814
id	SPR026848244
language_de	englisch
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author	Airola, Antti
spellingShingle	Airola, Antti misc Dependency Graph misc Training Corpus misc Percentage Unit misc Graph Kernel misc Tree Kernel All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning
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topic_title	All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning Dependency Graph (dpeaa)DE-He213 Training Corpus (dpeaa)DE-He213 Percentage Unit (dpeaa)DE-He213 Graph Kernel (dpeaa)DE-He213 Tree Kernel (dpeaa)DE-He213
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title	All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning
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title_full	All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning
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author_browse	Airola, Antti Pyysalo, Sampo Björne, Jari Pahikkala, Tapio Ginter, Filip Salakoski, Tapio
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title_sort	all-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning
title_auth	All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning
abstract	Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. © Airola et al; licensee BioMed Central Ltd. 2008
abstractGer	Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. © Airola et al; licensee BioMed Central Ltd. 2008
abstract_unstemmed	Background Automated extraction of protein-protein interactions (PPI) is an important and widely studied task in biomedical text mining. We propose a graph kernel based approach for this task. In contrast to earlier approaches to PPI extraction, the introduced all-paths graph kernel has the capability to make use of full, general dependency graphs representing the sentence structure. Results We evaluate the proposed method on five publicly available PPI corpora, providing the most comprehensive evaluation done for a machine learning based PPI-extraction system. We additionally perform a detailed evaluation of the effects of training and testing on different resources, providing insight into the challenges involved in applying a system beyond the data it was trained on. Our method is shown to achieve state-of-the-art performance with respect to comparable evaluations, with 56.4 F-score and 84.8 AUC on the AImed corpus. Conclusion We show that the graph kernel approach performs on state-of-the-art level in PPI extraction, and note the possible extension to the task of extracting complex interactions. Cross-corpus results provide further insight into how the learning generalizes beyond individual corpora. Further, we identify several pitfalls that can make evaluations of PPI-extraction systems incomparable, or even invalid. These include incorrect cross-validation strategies and problems related to comparing F-score results achieved on different evaluation resources. Recommendations for avoiding these pitfalls are provided. © Airola et al; licensee BioMed Central Ltd. 2008
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title_short	All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning
url	https://dx.doi.org/10.1186/1471-2105-9-S11-S2
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author2	Pyysalo, Sampo Björne, Jari Pahikkala, Tapio Ginter, Filip Salakoski, Tapio
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All-paths graph kernel for protein-protein interaction extraction with evaluation of cross-corpus learning

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