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      Quality assurance and enrichment of biological and biomedical ontologies and terminologies

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          Abstract

          Biological and biomedical ontologies and terminologies are used to organize and store various domain-specific knowledge to provide standardization of terminology usage and to improve interoperability. The growing number of such ontologies and terminologies and their increasing adoption in clinical, research and healthcare settings call for effective and efficient quality assurance and semantic enrichment techniques of these ontologies and terminologies. In this editorial, we provide an introductory summary of nine articles included in this supplement issue for quality assurance and enrichment of biological and biomedical ontologies and terminologies. The articles cover a range of standards including SNOMED CT, National Cancer Institute Thesaurus, Unified Medical Language System, North American Association of Central Cancer Registries and OBO Foundry Ontologies.

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          The Gene Ontology Resource: 20 years and still GOing strong

          Jurg Bahler (2019)
          Abstract The Gene Ontology resource (GO; http://geneontology.org) provides structured, computable knowledge regarding the functions of genes and gene products. Founded in 1998, GO has become widely adopted in the life sciences, and its contents are under continual improvement, both in quantity and in quality. Here, we report the major developments of the GO resource during the past two years. Each monthly release of the GO resource is now packaged and given a unique identifier (DOI), enabling GO-based analyses on a specific release to be reproduced in the future. The molecular function ontology has been refactored to better represent the overall activities of gene products, with a focus on transcription regulator activities. Quality assurance efforts have been ramped up to address potentially out-of-date or inaccurate annotations. New evidence codes for high-throughput experiments now enable users to filter out annotations obtained from these sources. GO-CAM, a new framework for representing gene function that is more expressive than standard GO annotations, has been released, and users can now explore the growing repository of these models. We also provide the ‘GO ribbon’ widget for visualizing GO annotations to a gene; the widget can be easily embedded in any web page.
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            NCI Thesaurus: a semantic model integrating cancer-related clinical and molecular information.

            Nicholas Sioutos, Sherri De Coronado, Margaret W Haber … (2007)
            Over the last 8 years, the National Cancer Institute (NCI) has launched a major effort to integrate molecular and clinical cancer-related information within a unified biomedical informatics framework, with controlled terminology as its foundational layer. The NCI Thesaurus is the reference terminology underpinning these efforts. It is designed to meet the growing need for accurate, comprehensive, and shared terminology, covering topics including: cancers, findings, drugs, therapies, anatomy, genes, pathways, cellular and subcellular processes, proteins, and experimental organisms. The NCI Thesaurus provides a partial model of how these things relate to each other, responding to actual user needs and implemented in a deductive logic framework that can help maintain the integrity and extend the informational power of what is provided. This paper presents the semantic model for cancer diseases and its uses in integrating clinical and molecular knowledge, more briefly examines the models and uses for drug, biochemical pathway, and mouse terminology, and discusses limits of the current approach and directions for future work.
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              The role of ontologies in biological and biomedical research: a functional perspective

              Robert Hoehndorf, Paul N. Schofield, Georgios Gkoutos (2015)
              Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that enable computational access to some aspects of the meaning of classes and relations. While each of these features enables applications that facilitate data integration, data access and analysis, a great potential lies in the possibility of combining these four features to support integrative analysis and interpretation of multimodal data. Here, we provide a functional perspective on ontologies in biology and biomedicine, focusing on what ontologies can do and describing how they can be used in support of integrative research. We also outline perspectives for using ontologies in data-driven science, in particular their application in structured data mining and machine learning applications.
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                Author and article information

                Contributors
                Licong Cui: licong.cui@uth.tmc.edu
                Journal
                Journal ID (nlm-ta): BMC Med Inform Decis Mak
                Journal ID (iso-abbrev): BMC Med Inform Decis Mak
                Title: BMC Medical Informatics and Decision Making
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1472-6947
                Publication date (Electronic): 15 December 2020
                Publication date PMC-release: 15 December 2020
                Publication date Collection: 2020
                Volume: 20
                Issue : Suppl 10 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. Supplement editors were not involved in the peer review of any manuscript that they co-authored. The Supplement Editors declare that they have no other competing interests.
                Electronic Location Identifier: 301
                Affiliations
                [1 ]GRID grid.259586.5, ISNI 0000 0001 0423 2931, Department of Computer Science, , Manhattan College, ; New York, USA
                [2 ]GRID grid.267308.8, ISNI 0000 0000 9206 2401, School of Biomedical Informatics, , University of Texas Health Science Center at Houston, ; Houston, TX USA
                Article
                Publisher ID: 1342
                DOI: 10.1186/s12911-020-01342-4
                PMC ID: 7737253
                PubMed ID: 33319696
                SO-VID: bfde49a7-e0b8-4f3d-b638-4fe4f0fa6300
                Copyright © © The Author(s) 2020
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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                Subject: Introduction
                Custom metadata
                issue-copyright-statement © The Author(s) 2020

                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: quality assurance,auditing,semantic enrichment,mapping,ontology
                Data availability:
                ScienceOpen disciplines: Bioinformatics & Computational biology
                Keywords: quality assurance, auditing, semantic enrichment, mapping, ontology

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