Progress toward a universal biomedical data translator
Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well‐being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline‐...
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| Vydané v: | Clinical and translational science Ročník 15; číslo 8; s. 1838 - 1847 |
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| Hlavní autori: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
United States
John Wiley & Sons, Inc
01.08.2022
Wiley John Wiley and Sons Inc |
| Predmet: | |
| ISSN: | 1752-8054, 1752-8062, 1752-8062 |
| On-line prístup: | Získať plný text |
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| Abstract | Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well‐being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline‐specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph‐based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real‐world use cases developed in collaboration with subject‐matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state‐of‐the‐art, biomedical graph‐based question‐answering systems. |
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| AbstractList | Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based "Translator" system capable of integrating existing biomedical data sets and "translating" those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system's architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems. Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems. Abstract Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well‐being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline‐specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph‐based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real‐world use cases developed in collaboration with subject‐matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state‐of‐the‐art, biomedical graph‐based question‐answering systems. |
| Audience | Academic |
| Author | Corkill, Dan Sinha, Meghamala Brandes, Mac Kenzie Whitlock, Jordan Santos, Eugene Viola, Adam Yao, Yao McMurry, Julie Mendoza, Luis Callaghan, Jackson Tropsha, Alexander Patton, Michael McClelland, Jason Pfaff, Emily Kvarfordt, Lindsey Strasser, Michael “ Michi” Wei, Qi Ahalt, Stanley C. Henrickson, Jeff Conlin, Tom Mantilla, Michelle Brandon, Namdi Southern, Nicholas Baumgartner, Andrew Lee, Jay Williams, Andrew Price, Guthrie Balhoff, Jim Bizon, Chris Rubin, Irit Hoatlin, Maureen Mersmann, Marian Harris, Nomi L. Dančík, Vlado Hubal, Robert Raj, Shruti Kim, Keum Joo Scott, Erik Morton, Kenny Xin, Jiwen Kevin Zheng, Marissa Deutsch, Eric Xu, Colleen Pollard, Elizabeth Watkins, Paul B. Muller, Sandrine Fecho, Karamarie Muluka, Arun Teja Mamidi, Tarun Avila, Ricardo Baumgartner, William Belhu, Basazin Zhu, Qian Flannick, Jason Mauldin, Denise Shrestha, Manil Massung, Jeffrey Grotthuss, Marcin Su, Andrew I. Leigh, Margaret Crumbley, Mary E. Womack, Finn Reilly, Jason Kang, Jimin Wood, Erica Lin, Jason Crouse, Andrew Dumontier, Michel Kluge, Alexandria Know |
| AuthorAffiliation | 13 Tufts Clinical and Translational Science Institute Tufts Medical Center Boston Massachusetts USA 1 Renaissance Computing Institute University of North Carolina at Chapel Hill Chapel Hill North Carolina USA 14 Thayer School of Engineering Dartmouth College Hanover New Hampshire USA 5 Division of Preclinical Innovation, National Center for Advancing Translational Sciences National Institutes of Health Rockville Maryland USA 6 Department of Biomedical Informatics Columbia University New York New York USA 15 Department of Information Science, College of Computing and Informatics Drexel University Philadelphia Pennsylvania USA 11 Institute of Data Science Maastricht University Maastricht The Netherlands 16 Department of Integrative Structural and Computational Biology The Scripps Research Institute La Jolla California USA 12 Division of Environmental Genomics and Systems Biology Lawrence Berkeley National Laboratory Berkeley California USA 8 Hugh Kaul Precision Medicine Institute University of Al |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35611543$$D View this record in MEDLINE/PubMed https://www.osti.gov/servlets/purl/2470866$$D View this record in Osti.gov |
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| ContentType | Journal Article |
| Contributor | Corkill, Dan Brandes, Mac Kenzie McMurry, Julie Callaghan, Jackson Mendoza, Luis Brush, Matthew McClelland, Jason Kvarfordt, Lindsey Conlin, Tom Henrickson, Jeff Mantilla, Michelle Brandon, Namdi Baumgartner, Andrew Lee, Jay Balhoff, Jim Hoatlin, Maureen Chen, Mei-Jan Mersmann, Marian Hubal, Robert Kim, Keum Joo Morton, Kenny Deutsch, Eric Muller, Sandrine Bruskiewich, Richard Mamidi, Tarun Muluka, Arun Teja Avila, Ricardo Baumgartner, William Belhu, Basazin Flannick, Jason Mauldin, Denise Massung, Jeffrey Leigh, Margaret Bada, Michael Kang, Jimin Lin, Jason Crouse, Andrew Kluge, Alexandria Knowles, Michael Champion, James Caufield, J Harry Ma, Chunyu Cohen, Kevin Foksinska, Aleksandra M Fehrmann, Nathaniel Crowder, Camerron Crumbley, Mary E Liu, Zheng Chen, Zhehuan Dai, Cheng Gardner, Vicki Moxon, Sierra A T Koesterer, Ryan Alden, John Costanzo, Maria Cox, Steven Mesbah, Abrar Korn, Daniel Acevedo, Liliana Goel, Prateek Carrell, Steven Huellas-Bruskiewicz, Kenneth Haaland, Perry Ahalt, Stanley C Huang, Conrad Magis, Andrew Alkanaq, Ahmed Haendel, Mel |
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| Copyright | 2022 The Authors. published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. 2022 The Authors. Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics. COPYRIGHT 2022 John Wiley & Sons, Inc. 2022. This work is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Notes | Funding information This work was supported by the National Center for Advancing Translational Sciences, Biomedical Data Translator Program (Other Transaction Awards OT2TR003434, OT2TR003436, OT2TR003428, OT2TR003448, OT2TR003427, OT2TR003430, OT2TR003433, OT2TR003450, OT2TR003437, OT2TR003443, OT2TR003441, OT2TR003449, OT2TR003445, OT2TR003422, OT2TR003435, OT3TR002026, OT3TR002020, OT3TR002025, OT3TR002019, OT3TR002027, OT2TR002517, OT2TR002514, OT2TR002515, OT2TR002584, and OT2TR002520; Contract number 75N95021P00636). Additional funding was provided by the National Center for Advancing Translational Sciences, Intramural Research Program (ZIA TR000276‐05) and the National Institute of Diabetes and Digestive and Kidney Diseases (5U01DK065201). Consortial/collaborative authors. These authors served as co‐first authors. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 ObjectType-Review-3 content type line 23 USDOE AC02-05CH11231 None Funding informationThis work was supported by the National Center for Advancing Translational Sciences, Biomedical Data Translator Program (Other Transaction Awards OT2TR003434, OT2TR003436, OT2TR003428, OT2TR003448, OT2TR003427, OT2TR003430, OT2TR003433, OT2TR003450, OT2TR003437, OT2TR003443, OT2TR003441, OT2TR003449, OT2TR003445, OT2TR003422, OT2TR003435, OT3TR002026, OT3TR002020, OT3TR002025, OT3TR002019, OT3TR002027, OT2TR002517, OT2TR002514, OT2TR002515, OT2TR002584, and OT2TR002520; Contract number 75N95021P00636). Additional funding was provided by the National Center for Advancing Translational Sciences, Intramural Research Program (ZIA TR000276‐05) and the National Institute of Diabetes and Digestive and Kidney Diseases (5U01DK065201). |
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| References | 2017; 1 2016; 3 2015; 3 2012 2020; 42 2019; 12 2019; 47 2022; 15 1999; 21 2016 2003; 18 2020; 87 2020; 201 1989; 16 2007; 33 2018; 54 2001; 25 e_1_2_8_28_1 e_1_2_8_29_1 e_1_2_8_24_1 e_1_2_8_25_1 e_1_2_8_26_1 e_1_2_8_27_1 Schmitt CP (e_1_2_8_9_1) 2015 e_1_2_8_3_1 e_1_2_8_2_1 e_1_2_8_5_1 e_1_2_8_4_1 e_1_2_8_7_1 Singhal A. (e_1_2_8_11_1) 2012 e_1_2_8_8_1 e_1_2_8_20_1 e_1_2_8_21_1 e_1_2_8_22_1 e_1_2_8_23_1 e_1_2_8_17_1 e_1_2_8_18_1 e_1_2_8_19_1 e_1_2_8_13_1 e_1_2_8_14_1 e_1_2_8_15_1 e_1_2_8_16_1 Ackoff RL (e_1_2_8_6_1) 1989; 16 e_1_2_8_32_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_12_1 e_1_2_8_30_1 |
| References_xml | – volume: 3 year: 2016 article-title: The FAIR guiding principles for scientific data management and stewardship publication-title: Sci Data – volume: 25 start-page: 107 issue: 1 year: 2001 end-page: 136 article-title: Review: knowledge management and knowledge management systems: conceptual foundations and research issues publication-title: Miss Q – volume: 42 start-page: 211 issue: 3 year: 2020 end-page: 220 article-title: Involvement of P450s and nuclear receptors in the hepatoprotective effect of quercetin on liver injury by bacterial lipopolysaccharide publication-title: Immunopharmacol Immunotoxicol – volume: 47 start-page: D948 issue: D1 year: 2019 end-page: D954 article-title: The comparative Toxicogenomics database: update 2019 publication-title: Nucleic Acids Res – volume: 87 start-page: 82 issue: 1 year: 2020 end-page: 88 article-title: Medication treatment of opioid use disorder publication-title: Biol Psychiatry – volume: 18 start-page: 1107 issue: 11–12 year: 2003 end-page: 1112 article-title: Antibiotic treatment of small bowel bacterial overgrowth in patients with Crohn's disease publication-title: Aliment Pharmacol Ther – volume: 54 start-page: 110 year: 2018 end-page: 112 article-title: Small intestinal bacterial overgrowth in Parkinson's disease: tribulations of a trial publication-title: Parkinsonism Relat Disord – volume: 12 start-page: 91 issue: 2 year: 2019 end-page: 94 article-title: The biomedical data translator program: conception, culture, and community publication-title: Clin Transl Sci – volume: 3 year: 2015 – volume: 21 start-page: 435 issue: 7 year: 1999 end-page: 443 article-title: Quercetin inhibits LPS‐induced nitric oxide and tumor necrosis factor‐alpha production in murine macrophages publication-title: Int J Immunopharmacol – volume: 201 start-page: 233 issue: 3 year: 2020 end-page: 243 article-title: Generation of a novel CD30+ B cell subset producing GM‐CSF and its possible link to the pathogenesis of systemic sclerosis publication-title: Clin Exp Immunol – volume: 16 start-page: 3 issue: 1 year: 1989 end-page: 9 article-title: From data to wisdom publication-title: J Appl Syst Anal – volume: 12 start-page: 86 issue: 2 year: 2019 end-page: 90 article-title: Toward a universal biomedical data translator publication-title: Clin Transl Sci – volume: 33 start-page: 163 issue: 2 year: 2007 end-page: 180 article-title: The wisdom hierarchy: representations of the DIKW hierarchy publication-title: J Inf Sci Eng – start-page: 1595 year: 2016 end-page: 1604 – volume: 12 start-page: 329 issue: 4 year: 2019 end-page: 333 article-title: Clinical data: sources and types, regulatory constraints, applications publication-title: Clin Transl Sci – volume: 1 start-page: 39 issue: 1–2 year: 2017 end-page: 57 article-title: The knowledge graph as the default data model for learning on heterogeneous knowledge publication-title: Data Sci – volume: 15 start-page: 1848 year: 2022 end-page: 1855 article-title: and The Biomedical Data Translator Consortium. Biolink model: a universal schema for knowledge graphs in clinical, biomedical, and translational science publication-title: Clin Transl Sci. – start-page: CD009281 issue: 3 year: 2012 article-title: Caffeine as an analgesic adjuvant for acute pain in adults publication-title: Cochrane Database Syst Rev – volume: 12 start-page: 85 issue: 2 year: 2019 article-title: Deconstructing the translational tower of babel publication-title: Clin Transl Sci – year: 2012 – ident: e_1_2_8_8_1 doi: 10.1177/0165551506070706 – ident: e_1_2_8_31_1 – volume-title: Introducing the Knowledge Graph: things, not strings year: 2012 ident: e_1_2_8_11_1 – ident: e_1_2_8_20_1 – ident: e_1_2_8_24_1 doi: 10.1111/cei.13477 – ident: e_1_2_8_14_1 – ident: e_1_2_8_17_1 – ident: e_1_2_8_3_1 doi: 10.1111/cts.12595 – ident: e_1_2_8_18_1 doi: 10.1111/cts.13302 – volume-title: Scientific Discovery in the Era of Big Data: More than the Scientific Method year: 2015 ident: e_1_2_8_9_1 – ident: e_1_2_8_32_1 – volume: 16 start-page: 3 issue: 1 year: 1989 ident: e_1_2_8_6_1 article-title: From data to wisdom publication-title: J Appl Syst Anal – ident: e_1_2_8_4_1 doi: 10.1111/cts.12591 – ident: e_1_2_8_13_1 doi: 10.1093/nar/gky868 – ident: e_1_2_8_21_1 – ident: e_1_2_8_16_1 – ident: e_1_2_8_23_1 doi: 10.1002/14651858.CD009281.pub3 – ident: e_1_2_8_19_1 – ident: e_1_2_8_26_1 doi: 10.1016/j.parkreldis.2018.04.003 – ident: e_1_2_8_28_1 doi: 10.1080/08923973.2020.174215428 – ident: e_1_2_8_10_1 doi: 10.3233/ds-170007 – ident: e_1_2_8_12_1 doi: 10.1145/2939672.2939815 – ident: e_1_2_8_27_1 doi: 10.1016/s0192-0561(99)00024-7 – ident: e_1_2_8_30_1 – ident: e_1_2_8_5_1 doi: 10.1111/cts.12592 – ident: e_1_2_8_15_1 doi: 10.1111/cts.12638 – ident: e_1_2_8_7_1 doi: 10.2307/3250961 – ident: e_1_2_8_25_1 doi: 10.1046/j.1365-2036.2003.01800.x – ident: e_1_2_8_22_1 doi: 10.1016/j.biopsych.2019.06.020 – ident: e_1_2_8_29_1 – ident: e_1_2_8_2_1 doi: 10.1038/sdata.2016.18 |
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| SubjectTerms | Application programming interface Consortia Electronic health records Interoperability Knowledge discovery Knowledge representation Mini‐review Natural language Ontology Pain Queries Research & Experimental Medicine Reviews Science Software Terminology Translation Well being |
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| Title | Progress toward a universal biomedical data translator |
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| Volume | 15 |
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