Annotating the human genome with Disease Ontology

<p<Abstract</p< <p<Background</p< <p<The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.</p< <p<Results</p< <p<We used the Unified Medical Language Sys...
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Autor*in:	Danila Maria I [verfasserIn] Zhu Lihua [verfasserIn] Kibbe Warren A [verfasserIn] Lin Simon M [verfasserIn] Holko Michelle [verfasserIn] Flatow Jared [verfasserIn] Osborne John D [verfasserIn] Feng Gang [verfasserIn] Chisholm Rex L [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2009

Übergeordnetes Werk:	In: BMC Genomics - BMC, 2003, 10(2009), Suppl 1, p S6
Übergeordnetes Werk:	volume:10 ; year:2009 ; number:Suppl 1, p S6

Links:	Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1186/1471-2164-10-S1-S6

Katalog-ID:	DOAJ064618889

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author_variant	d m i dmi z l zl k w a kwa l s m lsm h m hm f j fj o j d ojd f g fg c r l crl
matchkey_str	article:14712164:2009----::noaighhmneoeihi
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allfields	10.1186/1471-2164-10-S1-S6 doi (DE-627)DOAJ064618889 (DE-599)DOAJe6644c4233ec4f55954372575b2a37fc DE-627 ger DE-627 rakwb eng TP248.13-248.65 QH426-470 Danila Maria I verfasserin aut Annotating the human genome with Disease Ontology 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.</p< <p<Results</p< <p<We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.</p< <p<Conclusion</p< <p<The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.</p< Biotechnology Genetics Zhu Lihua verfasserin aut Kibbe Warren A verfasserin aut Lin Simon M verfasserin aut Holko Michelle verfasserin aut Flatow Jared verfasserin aut Osborne John D verfasserin aut Feng Gang verfasserin aut Chisholm Rex L verfasserin aut In BMC Genomics BMC, 2003 10(2009), Suppl 1, p S6 (DE-627)326644954 (DE-600)2041499-7 14712164 nnns volume:10 year:2009 number:Suppl 1, p S6 https://doi.org/10.1186/1471-2164-10-S1-S6 kostenfrei https://doaj.org/article/e6644c4233ec4f55954372575b2a37fc kostenfrei https://doaj.org/toc/1471-2164 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_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 10 2009 Suppl 1, p S6
spelling	10.1186/1471-2164-10-S1-S6 doi (DE-627)DOAJ064618889 (DE-599)DOAJe6644c4233ec4f55954372575b2a37fc DE-627 ger DE-627 rakwb eng TP248.13-248.65 QH426-470 Danila Maria I verfasserin aut Annotating the human genome with Disease Ontology 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.</p< <p<Results</p< <p<We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.</p< <p<Conclusion</p< <p<The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.</p< Biotechnology Genetics Zhu Lihua verfasserin aut Kibbe Warren A verfasserin aut Lin Simon M verfasserin aut Holko Michelle verfasserin aut Flatow Jared verfasserin aut Osborne John D verfasserin aut Feng Gang verfasserin aut Chisholm Rex L verfasserin aut In BMC Genomics BMC, 2003 10(2009), Suppl 1, p S6 (DE-627)326644954 (DE-600)2041499-7 14712164 nnns volume:10 year:2009 number:Suppl 1, p S6 https://doi.org/10.1186/1471-2164-10-S1-S6 kostenfrei https://doaj.org/article/e6644c4233ec4f55954372575b2a37fc kostenfrei https://doaj.org/toc/1471-2164 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_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 10 2009 Suppl 1, p S6
allfields_unstemmed	10.1186/1471-2164-10-S1-S6 doi (DE-627)DOAJ064618889 (DE-599)DOAJe6644c4233ec4f55954372575b2a37fc DE-627 ger DE-627 rakwb eng TP248.13-248.65 QH426-470 Danila Maria I verfasserin aut Annotating the human genome with Disease Ontology 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.</p< <p<Results</p< <p<We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.</p< <p<Conclusion</p< <p<The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.</p< Biotechnology Genetics Zhu Lihua verfasserin aut Kibbe Warren A verfasserin aut Lin Simon M verfasserin aut Holko Michelle verfasserin aut Flatow Jared verfasserin aut Osborne John D verfasserin aut Feng Gang verfasserin aut Chisholm Rex L verfasserin aut In BMC Genomics BMC, 2003 10(2009), Suppl 1, p S6 (DE-627)326644954 (DE-600)2041499-7 14712164 nnns volume:10 year:2009 number:Suppl 1, p S6 https://doi.org/10.1186/1471-2164-10-S1-S6 kostenfrei https://doaj.org/article/e6644c4233ec4f55954372575b2a37fc kostenfrei https://doaj.org/toc/1471-2164 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_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 10 2009 Suppl 1, p S6
allfieldsGer	10.1186/1471-2164-10-S1-S6 doi (DE-627)DOAJ064618889 (DE-599)DOAJe6644c4233ec4f55954372575b2a37fc DE-627 ger DE-627 rakwb eng TP248.13-248.65 QH426-470 Danila Maria I verfasserin aut Annotating the human genome with Disease Ontology 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.</p< <p<Results</p< <p<We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.</p< <p<Conclusion</p< <p<The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.</p< Biotechnology Genetics Zhu Lihua verfasserin aut Kibbe Warren A verfasserin aut Lin Simon M verfasserin aut Holko Michelle verfasserin aut Flatow Jared verfasserin aut Osborne John D verfasserin aut Feng Gang verfasserin aut Chisholm Rex L verfasserin aut In BMC Genomics BMC, 2003 10(2009), Suppl 1, p S6 (DE-627)326644954 (DE-600)2041499-7 14712164 nnns volume:10 year:2009 number:Suppl 1, p S6 https://doi.org/10.1186/1471-2164-10-S1-S6 kostenfrei https://doaj.org/article/e6644c4233ec4f55954372575b2a37fc kostenfrei https://doaj.org/toc/1471-2164 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_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 10 2009 Suppl 1, p S6
allfieldsSound	10.1186/1471-2164-10-S1-S6 doi (DE-627)DOAJ064618889 (DE-599)DOAJe6644c4233ec4f55954372575b2a37fc DE-627 ger DE-627 rakwb eng TP248.13-248.65 QH426-470 Danila Maria I verfasserin aut Annotating the human genome with Disease Ontology 2009 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier <p<Abstract</p< <p<Background</p< <p<The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.</p< <p<Results</p< <p<We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.</p< <p<Conclusion</p< <p<The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.</p< Biotechnology Genetics Zhu Lihua verfasserin aut Kibbe Warren A verfasserin aut Lin Simon M verfasserin aut Holko Michelle verfasserin aut Flatow Jared verfasserin aut Osborne John D verfasserin aut Feng Gang verfasserin aut Chisholm Rex L verfasserin aut In BMC Genomics BMC, 2003 10(2009), Suppl 1, p S6 (DE-627)326644954 (DE-600)2041499-7 14712164 nnns volume:10 year:2009 number:Suppl 1, p S6 https://doi.org/10.1186/1471-2164-10-S1-S6 kostenfrei https://doaj.org/article/e6644c4233ec4f55954372575b2a37fc kostenfrei https://doaj.org/toc/1471-2164 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_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 10 2009 Suppl 1, p S6
language	English
source	In BMC Genomics 10(2009), Suppl 1, p S6 volume:10 year:2009 number:Suppl 1, p S6
sourceStr	In BMC Genomics 10(2009), Suppl 1, p S6 volume:10 year:2009 number:Suppl 1, p S6
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Biotechnology Genetics
isfreeaccess_bool	true
container_title	BMC Genomics
authorswithroles_txt_mv	Danila Maria I @@aut@@ Zhu Lihua @@aut@@ Kibbe Warren A @@aut@@ Lin Simon M @@aut@@ Holko Michelle @@aut@@ Flatow Jared @@aut@@ Osborne John D @@aut@@ Feng Gang @@aut@@ Chisholm Rex L @@aut@@
publishDateDaySort_date	2009-01-01T00:00:00Z
hierarchy_top_id	326644954
id	DOAJ064618889
language_de	englisch
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callnumber-first	T - Technology
author	Danila Maria I
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topic_title	TP248.13-248.65 QH426-470 Annotating the human genome with Disease Ontology
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title	Annotating the human genome with Disease Ontology
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title_full	Annotating the human genome with Disease Ontology
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author_browse	Danila Maria I Zhu Lihua Kibbe Warren A Lin Simon M Holko Michelle Flatow Jared Osborne John D Feng Gang Chisholm Rex L
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title_sort	annotating the human genome with disease ontology
callnumber	TP248.13-248.65
title_auth	Annotating the human genome with Disease Ontology
abstract	<p<Abstract</p< <p<Background</p< <p<The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.</p< <p<Results</p< <p<We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.</p< <p<Conclusion</p< <p<The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.</p<
abstractGer	<p<Abstract</p< <p<Background</p< <p<The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.</p< <p<Results</p< <p<We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.</p< <p<Conclusion</p< <p<The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.</p<
abstract_unstemmed	<p<Abstract</p< <p<Background</p< <p<The human genome has been extensively annotated with Gene Ontology for biological functions, but minimally computationally annotated for diseases.</p< <p<Results</p< <p<We used the Unified Medical Language System (UMLS) MetaMap Transfer tool (MMTx) to discover gene-disease relationships from the GeneRIF database. We utilized a comprehensive subset of UMLS, which is disease-focused and structured as a directed acyclic graph (the Disease Ontology), to filter and interpret results from MMTx. The results were validated against the Homayouni gene collection using recall and precision measurements. We compared our results with the widely used Online Mendelian Inheritance in Man (OMIM) annotations.</p< <p<Conclusion</p< <p<The validation data set suggests a 91% recall rate and 97% precision rate of disease annotation using GeneRIF, in contrast with a 22% recall and 98% precision using OMIM. Our thesaurus-based approach allows for comparisons to be made between disease containing databases and allows for increased accuracy in disease identification through synonym matching. The much higher recall rate of our approach demonstrates that annotating human genome with Disease Ontology and GeneRIF for diseases dramatically increases the coverage of the disease annotation of human genome.</p<
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