1
|
Zhang H, Lyu T, Yin P, Bost S, He X, Guo Y, Prosperi M, Hogan WR, Bian J. A scoping review of semantic integration of health data and information. Int J Med Inform 2022; 165:104834. [PMID: 35863206 DOI: 10.1016/j.ijmedinf.2022.104834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 07/06/2022] [Accepted: 07/13/2022] [Indexed: 11/25/2022]
Abstract
OBJECTIVE We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains. MATERIALS AND METHODS We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review. RESULTS The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications. CONCLUSION SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.
Collapse
Affiliation(s)
- Hansi Zhang
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Tianchen Lyu
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Pengfei Yin
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Sarah Bost
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Xing He
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Yi Guo
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Mattia Prosperi
- Department of Epidemiology, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Willian R Hogan
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States
| | - Jiang Bian
- Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, United States.
| |
Collapse
|
2
|
Online visibility of software-related web sites: The case of biomedical text mining tools. Inf Process Manag 2019. [DOI: 10.1016/j.ipm.2018.11.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
|
3
|
Finding the right approach to big data-driven medicinal chemistry. Future Med Chem 2015; 7:1213-6. [DOI: 10.4155/fmc.15.58] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
|
4
|
Callahan A, Cifuentes JJ, Dumontier M. An evidence-based approach to identify aging-related genes in Caenorhabditis elegans. BMC Bioinformatics 2015; 16:40. [PMID: 25888240 PMCID: PMC4339751 DOI: 10.1186/s12859-015-0469-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2014] [Accepted: 01/15/2015] [Indexed: 12/21/2022] Open
Abstract
Background Extensive studies have been carried out on Caenorhabditis elegans as a model organism to elucidate mechanisms of aging and the effects of perturbing known aging-related genes on lifespan and behavior. This research has generated large amounts of experimental data that is increasingly difficult to integrate and analyze with existing databases and domain knowledge. To address this challenge, we demonstrate a scalable and effective approach for automatic evidence gathering and evaluation that leverages existing experimental data and literature-curated facts to identify genes involved in aging and lifespan regulation in C. elegans. Results We developed a semantic knowledge base for aging by integrating data about C. elegans genes from WormBase with data about 2005 human and model organism genes from GenAge and 149 genes from GenDR, and with the Bio2RDF network of linked data for the life sciences. Using HyQue (a Semantic Web tool for hypothesis-based querying and evaluation) to interrogate this knowledge base, we examined 48,231 C. elegans genes for their role in modulating lifespan and aging. HyQue identified 24 novel but well-supported candidate aging-related genes for further experimental validation. Conclusions We use semantic technologies to discover candidate aging genes whose effects on lifespan are not yet well understood. Our customized HyQue system, the aging research knowledge base it operates over, and HyQue evaluations of all C. elegans genes are freely available at http://hyque.semanticscience.org. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0469-4) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Alison Callahan
- Stanford Center for Biomedical Informatics Research, School of Medicine, Stanford University, Stanford California, AC, USA.
| | - Juan José Cifuentes
- Molecular Bioinformatics Laboratory, Millennium Institute on Immunology and Immunotherapy, 49 Santiago, CP, 8330025, Portugal. .,Departamento de Genética Molecular y Microbiología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Alameda 340, Santiago, Chile.
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, School of Medicine, Stanford University, Stanford California, AC, USA.
| |
Collapse
|
5
|
Application of text mining in the biomedical domain. Methods 2015; 74:97-106. [PMID: 25641519 DOI: 10.1016/j.ymeth.2015.01.015] [Citation(s) in RCA: 76] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Revised: 01/21/2015] [Accepted: 01/23/2015] [Indexed: 12/12/2022] Open
Abstract
In recent years the amount of experimental data that is produced in biomedical research and the number of papers that are being published in this field have grown rapidly. In order to keep up to date with developments in their field of interest and to interpret the outcome of experiments in light of all available literature, researchers turn more and more to the use of automated literature mining. As a consequence, text mining tools have evolved considerably in number and quality and nowadays can be used to address a variety of research questions ranging from de novo drug target discovery to enhanced biological interpretation of the results from high throughput experiments. In this paper we introduce the most important techniques that are used for a text mining and give an overview of the text mining tools that are currently being used and the type of problems they are typically applied for.
Collapse
|
6
|
Machado CM, Rebholz-Schuhmann D, Freitas AT, Couto FM. The semantic web in translational medicine: current applications and future directions. Brief Bioinform 2015; 16:89-103. [PMID: 24197933 PMCID: PMC4293377 DOI: 10.1093/bib/bbt079] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2013] [Accepted: 10/08/2013] [Indexed: 11/14/2022] Open
Abstract
Semantic web technologies offer an approach to data integration and sharing, even for resources developed independently or broadly distributed across the web. This approach is particularly suitable for scientific domains that profit from large amounts of data that reside in the public domain and that have to be exploited in combination. Translational medicine is such a domain, which in addition has to integrate private data from the clinical domain with proprietary data from the pharmaceutical domain. In this survey, we present the results of our analysis of translational medicine solutions that follow a semantic web approach. We assessed these solutions in terms of their target medical use case; the resources covered to achieve their objectives; and their use of existing semantic web resources for the purposes of data sharing, data interoperability and knowledge discovery. The semantic web technologies seem to fulfill their role in facilitating the integration and exploration of data from disparate sources, but it is also clear that simply using them is not enough. It is fundamental to reuse resources, to define mappings between resources, to share data and knowledge. All these aspects allow the instantiation of translational medicine at the semantic web-scale, thus resulting in a network of solutions that can share resources for a faster transfer of new scientific results into the clinical practice. The envisioned network of translational medicine solutions is on its way, but it still requires resolving the challenges of sharing protected data and of integrating semantic-driven technologies into the clinical practice.
Collapse
Affiliation(s)
- Catia M. Machado
- *Corresponding author. Catia M. Machado, Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, Portugal and Instituto de Engenharia de Sistemas e Computadores - Investigação e Desenvolvimento, Universidade de Lisboa, Portugal. E-mail:
| | | | | | | |
Collapse
|
7
|
Rebholz-Schuhmann D, Grabmüller C, Kavaliauskas S, Croset S, Woollard P, Backofen R, Filsell W, Clark D. A case study: semantic integration of gene-disease associations for type 2 diabetes mellitus from literature and biomedical data resources. Drug Discov Today 2013; 19:882-9. [PMID: 24201223 DOI: 10.1016/j.drudis.2013.10.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2012] [Revised: 09/24/2013] [Accepted: 10/28/2013] [Indexed: 10/26/2022]
Abstract
In the Semantic Enrichment of the Scientific Literature (SESL) project, researchers from academia and from life science and publishing companies collaborated in a pre-competitive way to integrate and share information for type 2 diabetes mellitus (T2DM) in adults. This case study exposes benefits from semantic interoperability after integrating the scientific literature with biomedical data resources, such as UniProt Knowledgebase (UniProtKB) and the Gene Expression Atlas (GXA). We annotated scientific documents in a standardized way, by applying public terminological resources for diseases and proteins, and other text-mining approaches. Eventually, we compared the genetic causes of T2DM across the data resources to demonstrate the benefits from the SESL triple store. Our solution enables publishers to distribute their content with little overhead into remote data infrastructures, such as into any Virtual Knowledge Broker.
Collapse
Affiliation(s)
- Dietrich Rebholz-Schuhmann
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Computerlinguistik, Universität Zürich, Binzmühlestrasse 14, 8050 Zürich, Switzerland.
| | - Christoph Grabmüller
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Silvestras Kavaliauskas
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Samuel Croset
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Peter Woollard
- GlaxoSmithKline, GlaxoSmithKline Medicines Research Centre, Gunnels Wood Road, Stevenage SG1 2NY, UK
| | - Rolf Backofen
- Albert-Ludwigs-University Freiburg, Fahnenbergplatz, D-79085 Freiburg, Germany
| | - Wendy Filsell
- Unilever R&D, Colworth Science Park, Sharnbrook MK44 1LQ, UK
| | - Dominic Clark
- European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| |
Collapse
|
8
|
Rebholz-Schuhmann D, Kim JH, Yan Y, Dixit A, Friteyre C, Hoehndorf R, Backofen R, Lewin I. Evaluation and cross-comparison of lexical entities of biological interest (LexEBI). PLoS One 2013; 8:e75185. [PMID: 24124474 PMCID: PMC3790750 DOI: 10.1371/journal.pone.0075185] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2012] [Accepted: 08/14/2013] [Indexed: 01/12/2023] Open
Abstract
MOTIVATION Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical "term space" (the "Lexeome"), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to the correct database entry. This goal does not only require that we are aware of all existing terms, but would also profit from knowing all their senses and their semantic interpretation (ambiguities, nestedness). RESULT This study compiles a resource for lexical terms of biomedical interest in a standard format (called "LexEBI"), determines the overall number of terms, their reuse in different resources and the nestedness of terms. LexEBI comprises references for protein and gene entries and their term variants and chemical entities amongst other terms. In addition, disease terms have been identified from Medline and PubmedCentral and added to LexEBI. Our analysis demonstrates that the baseforms of terms from the different semantic types show only little polysemous use. Nonetheless, the term variants of protein and gene names (PGNs) frequently contain species mentions, which should have been avoided according to protein annotation guidelines. Furthermore, the protein and gene entities as well as the chemical entities, both do comprise enzymes leading to hierarchical polysemy, and a large portion of PGNs make reference to a chemical entity. Altogether, according to our analysis based on the Medline distribution, 401,869 unique PGNs in the documents contain a reference to 25,022 chemical entities, 3,125 disease terms or 1,576 species mentions. CONCLUSION LexEBI delivers the complete biomedical and chemical Lexeome in a standardized representation (http://www.ebi.ac.uk/Rebholz-srv/LexEBI/). The resource provides the disease terms as open source content, and fully interlinks terms across resources.
Collapse
Affiliation(s)
- Dietrich Rebholz-Schuhmann
- Department of Computational Linguistics, University of Zürich, Zürich, Switzerland
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
- * E-mail:
| | - Jee-Hyub Kim
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Ying Yan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Abhishek Dixit
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Caroline Friteyre
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Robert Hoehndorf
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, United Kingdom
| | - Rolf Backofen
- Albert-Ludwigs-University Freiburg, Fahnenbergplatz, Freiburg, Germany
| | - Ian Lewin
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| |
Collapse
|