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Applied Sciences
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Application of Ontologies and Semantic Web Technologies in Biomedical Science
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Application of Ontologies and Semantic Web Technologies in Biomedical Science

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Biosciences and Bioengineering".

Deadline for manuscript submissions: closed (15 April 2022) | Viewed by 10188

Special Issue Editors

Prof. Dr. Catalina Martinez-Costa

E-Mail Website
Guest Editor
Department of Informatics and Systems, Faculty of Computer Science, Campus de Espinardo, University of Murcia, CP 30100 Murcia, Spain
Interests: semantic interoperability; electronic health record standards; knowledge representation; semantic web; biomedical ontologies; SNOMED CT
Prof. Dr. Jesualdo Tomás Fernández Breis

E-Mail Website
Guest Editor
Department of Informatics and Systems, Faculty of Computer Science, Campus de Espinardo, University of Murcia, CP 30100 Murcia, Spain
Interests: semantic web; ontologies; biomedical semantics; semantic interoperability; ontology engineering

Special Issue Information

Dear Colleagues,

Biomedical researchers face the challenge to integrate and analyze growing amounts of data across syntactically and semantically heterogeneous data sources. Semantic Web technologies and ontologies in particular are the main vehicle for data sharing, integration, and reuse. They enable the formal representation of data meaning, which is indispensable for their unambiguous interpretation.

Examples of prominent ontologies in the biomedical science are gene ontology and SNOMED CT. In addition, the OBO Foundry community and the BioPortal repository are two initiatives that show the increasing adoption of ontologies and Semantic Web technologies in the biomedical domain.

Linked data and knowledge graphs are also key components of the Semantic Web. While the former pursues the open sharing of data through RDF graphs, knowledge graphs have a less open perspective and have already found a place in industry with companies such as Google, Microsoft, IBM, etc.

Ontologies and Semantic Web technologies are related to many disciplines such as knowledge representation and reasoning or databases and are being applied successfully to other fields such as natural language processing or machine learning.

This Special Issue aims to reflect the state of the art in these technologies and to provide guidance on future research directions. Submissions include research papers, case studies, and reviews.

Topics of interest include but are not limited to the following areas:

  • Knowledge representation and reasoning;
  • Ontology development and enrichment;
  • Ontology and linked data set quality assurance;
  • Semantic harmonization and ontology alignment;
  • Knowledge graphs;
  • Knowledge representation systems in life sciences and medicine;
  • NLP and text mining using semantic technologies;
  • Novel approaches for data integration of heterogeneous data sources;
  • Novel tools and ontologies for data interpretation and visualization;
  • Deep learning and semantic technologies;
  • Real-world ontology-based applications;
  • Methods and tools for the FAIRification of datasets;
  • Biomedical research objects;
  • Industry use cases;
  • Artificial intelligence techniques for the biomedical Semantic Web;
  • Ontologies and Semantic web for decision support;
  • Semantic technologies and AI explainability in biomedicine.

Prof. Dr. Catalina Martinez-Costa
Prof. Dr. Jesualdo Tomás Fernández Breis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Published Papers (3 papers)

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Research

8 pages, 485 KiB  
Article
Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach
by Hua Min, Farrokh Alemi, Christopher A. Hane and Vijay S. Nori
Appl. Sci. 2022, 12(3), 1479; https://doi.org/10.3390/app12031479 - 29 Jan 2022
Cited by 4 | Viewed by 3124
Abstract
For patients with rare comorbidities, there are insufficient observations to accurately estimate the effectiveness of treatment. At the same time, all diagnosis, including rare diagnosis, are part of the International Classification of Disease (ICD). Grouping ICD into broader concepts (i.e., ontology adjustment) can [...] Read more.
For patients with rare comorbidities, there are insufficient observations to accurately estimate the effectiveness of treatment. At the same time, all diagnosis, including rare diagnosis, are part of the International Classification of Disease (ICD). Grouping ICD into broader concepts (i.e., ontology adjustment) can not only increase accuracy of estimating antidepressant effectiveness for patients with rare conditions but also prevent overfitting in big data analysis. In this study, 3,678,082 depressed patients treated with antidepressants were obtained from OptumLabs® Data Warehouse (OLDW). For rare diagnoses, adjustments were made by using the likelihood ratio of the immediate broader concept in the ICD hierarchies. The accuracy of models in training (90%) and test (10%) sets was examined using the area under the receiver operating curves (AROC). The gap in training and test AROC shows how much random noise was modeled. If the gap is large, then the parameters of the model, including the reported effectiveness of the antidepressant for patients with rare conditions, are suspect. There was, on average, a 9.0% reduction in the AROC gap after using the ontological adjustment. Therefore, ontology adjustment can reduce model overfitting, leading to better parameter estimates from the training set. Full article
(This article belongs to the Special Issue Application of Ontologies and Semantic Web Technologies in Biomedical Science)
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16 pages, 1744 KiB  
Article
An Ontology-Driven Learning Assessment Using the Script Concordance Test
by Maja Radovic, Nenad Petrovic and Milorad Tosic
Appl. Sci. 2022, 12(3), 1472; https://doi.org/10.3390/app12031472 - 29 Jan 2022
Cited by 3 | Viewed by 3042
Abstract
Assessing the level of domain-specific reasoning acquired by students is one of the major challenges in education particularly in medical education. Considering the importance of clinical reasoning in preclinical and clinical practice, it is necessary to evaluate students’ learning achievements accordingly. The traditional [...] Read more.
Assessing the level of domain-specific reasoning acquired by students is one of the major challenges in education particularly in medical education. Considering the importance of clinical reasoning in preclinical and clinical practice, it is necessary to evaluate students’ learning achievements accordingly. The traditional way of assessing clinical reasoning includes long-case exams, oral exams, and objective structured clinical examinations. However, the traditional assessment techniques are not enough to answer emerging requirements in the new reality due to limited scalability and difficulty for adoption in online education. In recent decades, the script concordance test (SCT) has emerged as a promising tool for assessment, particularly in medical education. The question is whether the usability of SCT could be raised to a level high enough to match the current education requirements by exploiting opportunities that new technologies provide, particularly semantic knowledge graphs (SCGs) and ontologies. In this paper, an ontology-driven learning assessment is proposed using a novel automated SCT generation platform. SCTonto ontology is adopted for knowledge representation in SCT question generation with the focus on using electronic health records data for medical education. Direct and indirect strategies for generating Likert-type scores of SCT are described in detail as well. The proposed automatic question generation was evaluated against the traditional manually created SCT, and the results showed that the time required for tests creation significantly reduced, which confirms significant scalability improvements with respect to traditional approaches. Full article
(This article belongs to the Special Issue Application of Ontologies and Semantic Web Technologies in Biomedical Science)
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29 pages, 12140 KiB  
Article
An Ontology to Model the International Rules for Multiple Primary Malignant Tumours in Cancer Registration
by Nicholas Charles Nicholson, Francesco Giusti, Manola Bettio, Raquel Negrao Carvalho, Nadya Dimitrova, Tadeusz Dyba, Manuela Flego, Luciana Neamtiu, Giorgia Randi and Carmen Martos
Appl. Sci. 2021, 11(16), 7233; https://doi.org/10.3390/app11167233 - 5 Aug 2021
Cited by 5 | Viewed by 2323
Abstract
Population-based cancer registry data provide a key epidemiological resource for monitoring cancer in defined populations. Validation of the data variables contributing to a common data set is necessary to remove statistical bias; the process is currently performed centrally. An ontology-based approach promises advantages [...] Read more.
Population-based cancer registry data provide a key epidemiological resource for monitoring cancer in defined populations. Validation of the data variables contributing to a common data set is necessary to remove statistical bias; the process is currently performed centrally. An ontology-based approach promises advantages in devolving the validation process to the registry level but the checks regarding multiple primary tumours have presented a hurdle. This work presents a solution by modelling the international rules for multiple primary cancers in description logic. Topography groupings described in the rules had to be further categorised in order to simplify the axioms. Description logic expressivity was constrained as far as possible for reasons of automatic reasoning performance. The axioms were consistently able to trap all the different types of scenarios signalling violation of the rules. Batch processing of many records were performed using the Web Ontology Language application programme interface. Performance issues were circumvented for large data sets using the software interface to perform the reasoning operations on the basis of the axioms encoded in the ontology. These results remove one remaining hurdle in developing a purely ontology-based solution for performing the European harmonised data-quality checks, with a number of inherent advantages including the formalisation and integration of the validation rules within the domain data model itself. Full article
(This article belongs to the Special Issue Application of Ontologies and Semantic Web Technologies in Biomedical Science)
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