Literature DB >> 22874154

An information artifact ontology perspective on data collections and associated representational artifacts.

Werner Ceusters1.   

Abstract

Biomedical data collections are typically compiled on the basis of assessment instruments and associated terminologies and their data structure explained by means of data dictionaries. The Information Artifact Ontology (IAO) is an attempt to give a realism-based account of the essence of information entities and how components of such entities relate to each other and to that what they are information about. Changes in the taxonomy and the definitions of the IAO, most importantly the addition of the terms 'representational artifact' and 'representational unit', are proposed to make the IAO a useful tool to clarify formally the distinctions and commonalities between data collections and associated artifacts that are compiled independently from each other, yet cover the same domain.

Mesh:

Year:  2012        PMID: 22874154

Source DB:  PubMed          Journal:  Stud Health Technol Inform        ISSN: 0926-9630


  12 in total

1.  Improving the 'Fitness for Purpose' of Common Data Models through Realism Based Ontology.

Authors:  Jonathan C Blaisure; Werner M Ceusters
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

2.  An empirical analysis of ontology reuse in BioPortal.

Authors:  Christopher Ochs; Yehoshua Perl; James Geller; Sivaram Arabandi; Tania Tudorache; Mark A Musen
Journal:  J Biomed Inform       Date:  2017-06-02       Impact factor: 6.317

3.  Dental EHR-infused Persona Ontologies to Enrich Dental Dialogue Interaction of Agents.

Authors:  Patricia Ngantcha; Muhammad Tuan Amith; Kirk Roberts; John A Valenza; Muhammad Walji; Cui Tao
Journal:  Proceedings (IEEE Int Conf Bioinformatics Biomed)       Date:  2021-12

4.  MetaboListem and TABoLiSTM: Two Deep Learning Algorithms for Metabolite Named Entity Recognition.

Authors:  Cheng S Yeung; Tim Beck; Joram M Posma
Journal:  Metabolites       Date:  2022-03-22

5.  Linking MedDRA(®)-Coded Clinical Phenotypes to Biological Mechanisms by the Ontology of Adverse Events: A Pilot Study on Tyrosine Kinase Inhibitors.

Authors:  Sirarat Sarntivijai; Shelley Zhang; Desikan G Jagannathan; Shadia Zaman; Keith K Burkhart; Gilbert S Omenn; Yongqun He; Brian D Athey; Darrell R Abernethy
Journal:  Drug Saf       Date:  2016-07       Impact factor: 5.606

6.  EXACT2: the semantics of biomedical protocols.

Authors:  Larisa N Soldatova; Daniel Nadis; Ross D King; Piyali S Basu; Emma Haddi; Véronique Baumlé; Nigel J Saunders; Wolfgang Marwan; Brian B Rudkin
Journal:  BMC Bioinformatics       Date:  2014-11-27       Impact factor: 3.169

7.  Emerging semantics to link phenotype and environment.

Authors:  Anne E Thessen; Daniel E Bunker; Pier Luigi Buttigieg; Laurel D Cooper; Wasila M Dahdul; Sami Domisch; Nico M Franz; Pankaj Jaiswal; Carolyn J Lawrence-Dill; Peter E Midford; Christopher J Mungall; Martín J Ramírez; Chelsea D Specht; Lars Vogt; Rutger Aldo Vos; Ramona L Walls; Jeffrey W White; Guanyang Zhang; Andrew R Deans; Eva Huala; Suzanna E Lewis; Paula M Mabee
Journal:  PeerJ       Date:  2015-12-14       Impact factor: 2.984

8.  An ontological analysis of medical Bayesian indicators of performance.

Authors:  Adrien Barton; Jean-François Ethier; Régis Duvauferrier; Anita Burgun
Journal:  J Biomed Semantics       Date:  2017-01-03

9.  Integrating herbarium specimen observations into global phenology data systems.

Authors:  Laura Brenskelle; Brian J Stucky; John Deck; Ramona Walls; Rob P Guralnick
Journal:  Appl Plant Sci       Date:  2019-03-07       Impact factor: 1.936

10.  Ontology-guided segmentation and object identification for developmental mouse lung immunofluorescent images.

Authors:  Anna Maria Masci; Scott White; Ben Neely; Maryanne Ardini-Polaske; Carol B Hill; Ravi S Misra; Bruce Aronow; Nathan Gaddis; Lina Yang; Susan E Wert; Scott M Palmer; Cliburn Chan
Journal:  BMC Bioinformatics       Date:  2021-02-23       Impact factor: 3.307

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