Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify...
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Autor*in:	Charles C. N. Wang [verfasserIn] Jennifer Jin [verfasserIn] Jan-Gowth Chang [verfasserIn] Masahiro Hayakawa [verfasserIn] Atsushi Kitazawa [verfasserIn] Jeffrey J. P. Tsai [verfasserIn] Phillip C.-Y. Sheu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2020

Schlagwörter:	Gastrointestinal cancer Text mining Bi-LSTM-CNN-CRF Influence maximization Co-occurrence network

Übergeordnetes Werk:	In: BMC Medical Informatics and Decision Making - BMC, 2003, 20(2020), 1, Seite 12
Übergeordnetes Werk:	volume:20 ; year:2020 ; number:1 ; pages:12

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/s12911-020-01227-6

Katalog-ID:	DOAJ051696037

Internformat


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520			\|a Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. Results The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.
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700	0		\|a Jeffrey J. P. Tsai \|e verfasserin \|4 aut
700	0		\|a Phillip C.-Y. Sheu \|e verfasserin \|4 aut
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allfields	10.1186/s12911-020-01227-6 doi (DE-627)DOAJ051696037 (DE-599)DOAJ343b4c817de14241a2cba689e5b26ec2 DE-627 ger DE-627 rakwb eng R858-859.7 Charles C. N. Wang verfasserin aut Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. Results The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications. Gastrointestinal cancer Text mining Bi-LSTM-CNN-CRF Influence maximization Co-occurrence network Computer applications to medicine. Medical informatics Jennifer Jin verfasserin aut Jan-Gowth Chang verfasserin aut Masahiro Hayakawa verfasserin aut Atsushi Kitazawa verfasserin aut Jeffrey J. P. Tsai verfasserin aut Phillip C.-Y. Sheu verfasserin aut In BMC Medical Informatics and Decision Making BMC, 2003 20(2020), 1, Seite 12 (DE-627)328977306 (DE-600)2046490-3 14726947 nnns volume:20 year:2020 number:1 pages:12 https://doi.org/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/article/343b4c817de14241a2cba689e5b26ec2 kostenfrei http://link.springer.com/article/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/toc/1472-6947 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_31 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_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 20 2020 1 12
spelling	10.1186/s12911-020-01227-6 doi (DE-627)DOAJ051696037 (DE-599)DOAJ343b4c817de14241a2cba689e5b26ec2 DE-627 ger DE-627 rakwb eng R858-859.7 Charles C. N. Wang verfasserin aut Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. Results The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications. Gastrointestinal cancer Text mining Bi-LSTM-CNN-CRF Influence maximization Co-occurrence network Computer applications to medicine. Medical informatics Jennifer Jin verfasserin aut Jan-Gowth Chang verfasserin aut Masahiro Hayakawa verfasserin aut Atsushi Kitazawa verfasserin aut Jeffrey J. P. Tsai verfasserin aut Phillip C.-Y. Sheu verfasserin aut In BMC Medical Informatics and Decision Making BMC, 2003 20(2020), 1, Seite 12 (DE-627)328977306 (DE-600)2046490-3 14726947 nnns volume:20 year:2020 number:1 pages:12 https://doi.org/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/article/343b4c817de14241a2cba689e5b26ec2 kostenfrei http://link.springer.com/article/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/toc/1472-6947 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_31 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_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 20 2020 1 12
allfields_unstemmed	10.1186/s12911-020-01227-6 doi (DE-627)DOAJ051696037 (DE-599)DOAJ343b4c817de14241a2cba689e5b26ec2 DE-627 ger DE-627 rakwb eng R858-859.7 Charles C. N. Wang verfasserin aut Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. Results The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications. Gastrointestinal cancer Text mining Bi-LSTM-CNN-CRF Influence maximization Co-occurrence network Computer applications to medicine. Medical informatics Jennifer Jin verfasserin aut Jan-Gowth Chang verfasserin aut Masahiro Hayakawa verfasserin aut Atsushi Kitazawa verfasserin aut Jeffrey J. P. Tsai verfasserin aut Phillip C.-Y. Sheu verfasserin aut In BMC Medical Informatics and Decision Making BMC, 2003 20(2020), 1, Seite 12 (DE-627)328977306 (DE-600)2046490-3 14726947 nnns volume:20 year:2020 number:1 pages:12 https://doi.org/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/article/343b4c817de14241a2cba689e5b26ec2 kostenfrei http://link.springer.com/article/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/toc/1472-6947 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_31 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_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 20 2020 1 12
allfieldsGer	10.1186/s12911-020-01227-6 doi (DE-627)DOAJ051696037 (DE-599)DOAJ343b4c817de14241a2cba689e5b26ec2 DE-627 ger DE-627 rakwb eng R858-859.7 Charles C. N. Wang verfasserin aut Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. Results The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications. Gastrointestinal cancer Text mining Bi-LSTM-CNN-CRF Influence maximization Co-occurrence network Computer applications to medicine. Medical informatics Jennifer Jin verfasserin aut Jan-Gowth Chang verfasserin aut Masahiro Hayakawa verfasserin aut Atsushi Kitazawa verfasserin aut Jeffrey J. P. Tsai verfasserin aut Phillip C.-Y. Sheu verfasserin aut In BMC Medical Informatics and Decision Making BMC, 2003 20(2020), 1, Seite 12 (DE-627)328977306 (DE-600)2046490-3 14726947 nnns volume:20 year:2020 number:1 pages:12 https://doi.org/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/article/343b4c817de14241a2cba689e5b26ec2 kostenfrei http://link.springer.com/article/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/toc/1472-6947 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_31 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_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 20 2020 1 12
allfieldsSound	10.1186/s12911-020-01227-6 doi (DE-627)DOAJ051696037 (DE-599)DOAJ343b4c817de14241a2cba689e5b26ec2 DE-627 ger DE-627 rakwb eng R858-859.7 Charles C. N. Wang verfasserin aut Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. Results The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications. Gastrointestinal cancer Text mining Bi-LSTM-CNN-CRF Influence maximization Co-occurrence network Computer applications to medicine. Medical informatics Jennifer Jin verfasserin aut Jan-Gowth Chang verfasserin aut Masahiro Hayakawa verfasserin aut Atsushi Kitazawa verfasserin aut Jeffrey J. P. Tsai verfasserin aut Phillip C.-Y. Sheu verfasserin aut In BMC Medical Informatics and Decision Making BMC, 2003 20(2020), 1, Seite 12 (DE-627)328977306 (DE-600)2046490-3 14726947 nnns volume:20 year:2020 number:1 pages:12 https://doi.org/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/article/343b4c817de14241a2cba689e5b26ec2 kostenfrei http://link.springer.com/article/10.1186/s12911-020-01227-6 kostenfrei https://doaj.org/toc/1472-6947 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_31 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_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 20 2020 1 12
language	English
source	In BMC Medical Informatics and Decision Making 20(2020), 1, Seite 12 volume:20 year:2020 number:1 pages:12
sourceStr	In BMC Medical Informatics and Decision Making 20(2020), 1, Seite 12 volume:20 year:2020 number:1 pages:12
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Gastrointestinal cancer Text mining Bi-LSTM-CNN-CRF Influence maximization Co-occurrence network Computer applications to medicine. Medical informatics
isfreeaccess_bool	true
container_title	BMC Medical Informatics and Decision Making
authorswithroles_txt_mv	Charles C. N. Wang @@aut@@ Jennifer Jin @@aut@@ Jan-Gowth Chang @@aut@@ Masahiro Hayakawa @@aut@@ Atsushi Kitazawa @@aut@@ Jeffrey J. P. Tsai @@aut@@ Phillip C.-Y. Sheu @@aut@@
publishDateDaySort_date	2020-01-01T00:00:00Z
hierarchy_top_id	328977306
id	DOAJ051696037
language_de	englisch
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Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2020</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. 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callnumber-first	R - Medicine
author	Charles C. N. Wang
spellingShingle	Charles C. N. Wang misc R858-859.7 misc Gastrointestinal cancer misc Text mining misc Bi-LSTM-CNN-CRF misc Influence maximization misc Co-occurrence network misc Computer applications to medicine. Medical informatics Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization
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topic_title	R858-859.7 Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization Gastrointestinal cancer Text mining Bi-LSTM-CNN-CRF Influence maximization Co-occurrence network
topic	misc R858-859.7 misc Gastrointestinal cancer misc Text mining misc Bi-LSTM-CNN-CRF misc Influence maximization misc Co-occurrence network misc Computer applications to medicine. Medical informatics
topic_unstemmed	misc R858-859.7 misc Gastrointestinal cancer misc Text mining misc Bi-LSTM-CNN-CRF misc Influence maximization misc Co-occurrence network misc Computer applications to medicine. Medical informatics
topic_browse	misc R858-859.7 misc Gastrointestinal cancer misc Text mining misc Bi-LSTM-CNN-CRF misc Influence maximization misc Co-occurrence network misc Computer applications to medicine. Medical informatics
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title	Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization
ctrlnum	(DE-627)DOAJ051696037 (DE-599)DOAJ343b4c817de14241a2cba689e5b26ec2
title_full	Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization
author_sort	Charles C. N. Wang
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author_browse	Charles C. N. Wang Jennifer Jin Jan-Gowth Chang Masahiro Hayakawa Atsushi Kitazawa Jeffrey J. P. Tsai Phillip C.-Y. Sheu
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author-letter	Charles C. N. Wang
doi_str_mv	10.1186/s12911-020-01227-6
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title_sort	identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization
callnumber	R858-859.7
title_auth	Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization
abstract	Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. Results The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.
abstractGer	Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. Results The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.
abstract_unstemmed	Abstract Background Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of co-occurring genes that are the most influential based on an influence maximization algorithm. Results The sets of co-occurring genes that are the most influential that we discover include RARA - CRBP1, CASP3 - BCL2, BCL2 - CASP3 – CRBP1, RARA - CASP3 – CRBP1, FOXJ1 - RASSF3 - ESR1, FOXJ1 - RASSF1A - ESR1, FOXJ1 - RASSF1A - TNFAIP8 - ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.
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title_short	Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization
url	https://doi.org/10.1186/s12911-020-01227-6 https://doaj.org/article/343b4c817de14241a2cba689e5b26ec2 http://link.springer.com/article/10.1186/s12911-020-01227-6 https://doaj.org/toc/1472-6947
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author2	Jennifer Jin Jan-Gowth Chang Masahiro Hayakawa Atsushi Kitazawa Jeffrey J. P. Tsai Phillip C.-Y. Sheu
author2Str	Jennifer Jin Jan-Gowth Chang Masahiro Hayakawa Atsushi Kitazawa Jeffrey J. P. Tsai Phillip C.-Y. Sheu
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up_date	2024-07-03T21:47:31.394Z
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Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

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