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BioMedInformatics
Special Issues
Application of Semantic Web Technologies in Biomedicine and Biomedical Informatics
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Application of Semantic Web Technologies in Biomedicine and Biomedical Informatics

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Computational Biology and Medicine".

Deadline for manuscript submissions: closed (28 December 2023) | Viewed by 2602

Special Issue Editors

Dr. Pietro Hiram Guzzi

E-Mail Website
Guest Editor
1. Municipality of Miglierina, Street B., 88040 Telesio, Italy
2. Department of Surgical and Medical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
Interests: semantic web technologies in biomedicine and biomedical informatics; artificial intelligence and machine learning for data-centered bioinformatics; network analysis; intelligent mining of large-scale biomedical data; bioinformatics research and applications; data dimension reduction methods in high-dimensional space (e.g., embeddings, database); clinical research and bioinformatics; neural networks and their applications in bioinformatics; computational intelligence in data-centered bioinformatics; software tools and databases for semantic web technologies; high-performance computing in bioinformatics
Dr. Pietro Cinaglia

E-Mail Website
Guest Editor
Department of Health Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
Interests: semantic web technologies in biomedicine and biomedical informatics; artificial intelligence and machine learning for data-centered bioinformatics; network analysis; intelligent mining of large-scale biomedical data; bioinformatics research and applications; data dimension reduction methods in high-dimensional space (e.g., embeddings, database); clinical research and bioinformatics; neural networks and their applications in bioinformatics; computational intelligence in data-centered bioinformatics; software tools and databases for semantic web technologies; high-performance computing in bioinformatics

Special Issue Information

Dear Colleagues,

The large amount of biomedical data has allowed for the proliferation of novel methods for data analysis and knowledge extraction. Bioinformatics has focused attention on these, allowing data processing and analysis both of homogeneous and heterogeneous sources. The Semantic Web technologies are focused on interrelations that allow data computation and processing. Scientists apply these to reuse, integrate, and share data, better enabling a more effective knowledge extraction.

This Special Issue will publish papers on a wide range of data-centered bioinformatics topics by focusing on the semantic web technologies in biomedicine and biomedical informatics. It will consider articles describing novel computational algorithms and software tools, data models, and methodologies based on artificial intelligence and systems biology in biomedicine and biomedical informatics fields. Its main aim will be collecting original works in the fields of bioinformatics, databases, data science, and medical informatics, as well as in application-related fields (e.g., biology and medicine), to address the challenges and the requirements related to the application of Semantic Web technologies in biomedicine and biomedical informatics.

You may choose our Joint Special Issue in Life.

Dr. Pietro Hiram Guzzi
Dr. Pietro Cinaglia
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. BioMedInformatics is an international peer-reviewed open access quarterly 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 1000 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.

Keywords

  • semantic web technologies in biomedicine and biomedical informatics
  • artificial intelligence and machine learning for data-centered bioinformatics
  • network analysis
  • intelligent mining of large-scale biomedical data
  • bioinformatics research and applications
  • data dimension reduction methods in high-dimensional space (e.g., embeddings, database)
  • clinical research and bioinformatics
  • neural networks and their applications in bioinformatics
  • computational intelligence in data-centered bioinformatics
  • software tools and databases for semantic web technologies
  • high-performance computing in bioinformatics

Published Papers (3 papers)

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19 pages, 3556 KiB  
Article
Reliability and Agreement of Free Web-Based 3D Software for Computing Facial Area and Volume Measurements
by Oguzhan Topsakal, Philip Sawyer, Tahir Cetin Akinci, Elif Topsakal and M. Mazhar Celikoyar
BioMedInformatics 2024, 4(1), 690-708; https://doi.org/10.3390/biomedinformatics4010038 - 1 Mar 2024
Viewed by 514
Abstract
Background: Facial surgeries require meticulous planning and outcome assessments, where facial analysis plays a critical role. This study introduces a new approach by utilizing three-dimensional (3D) imaging techniques, which are known for their ability to measure facial areas and volumes accurately. The purpose [...] Read more.
Background: Facial surgeries require meticulous planning and outcome assessments, where facial analysis plays a critical role. This study introduces a new approach by utilizing three-dimensional (3D) imaging techniques, which are known for their ability to measure facial areas and volumes accurately. The purpose of this study is to introduce and evaluate a free web-based software application designed to take area and volume measurements on 3D models of patient faces. Methods: This study employed the online facial analysis software to conduct ten measurements on 3D models of subjects, including five measurements of area and five measurements of volume. These measurements were then compared with those obtained from the established 3D modeling software called Blender (version 3.2) using the Bland–Altman plot. To ensure accuracy, the intra-rater and inter-rater reliabilities of the web-based software were evaluated using the Intraclass Correlation Coefficient (ICC) method. Additionally, statistical assumptions such as normality and homoscedasticity were rigorously verified before analysis. Results: This study found that the web-based facial analysis software showed high agreement with the 3D software Blender within 95% confidence limits. Moreover, the online application demonstrated excellent intra-rater and inter-rater reliability in most analyses, as indicated by the ICC test. Conclusion: The findings suggest that the free online 3D software is reliable for facial analysis, particularly in measuring areas and volumes. This indicates its potential utility in enhancing surgical planning and evaluation in facial surgeries. This study underscores the software’s capability to improve surgical outcomes by integrating precise area and volume measurements into facial surgery planning and assessment processes. Full article
(This article belongs to the Special Issue Application of Semantic Web Technologies in Biomedicine and Biomedical Informatics)
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19 pages, 2823 KiB  
Article
Optimized FIR Filter Using Genetic Algorithms: A Case Study of ECG Signals Filter Optimization
by Houssam Hamici, Awos Kanan and Khalid Al-hammuri
BioMedInformatics 2023, 3(4), 1197-1215; https://doi.org/10.3390/biomedinformatics3040071 - 8 Dec 2023
Viewed by 821
Abstract
The advancement in technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to [...] Read more.
The advancement in technology and the availability of specialized digital signal processing chips have made digital filter design and implementation more feasible in a variety of fields, including biomedical engineering. This paper makes two key contributions. First, it uses a genetic algorithm to optimize the coefficients of finite impulse response (FIR) filters. Second, it conducts a case study on using genetic algorithms to optimize FIR filters for electrocardiogram (ECG) biomedical signal noise removal. The goal of the proposed filter design approach is to achieve the desired signal bandwidth while minimizing the side lobe level and eliminating unwanted signals using a genetic algorithm. The results of a comprehensive analysis show that the genetic algorithm-based filter is more effective than conventional filter designs in terms of noise removal efficiency. Full article
(This article belongs to the Special Issue Application of Semantic Web Technologies in Biomedicine and Biomedical Informatics)
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12 pages, 2619 KiB  
Article
Facilitating “Omics” for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics
by Uraquitan Lima Filho, Tiago Alexandre Pais and Ricardo Jorge Pais
BioMedInformatics 2023, 3(4), 1071-1082; https://doi.org/10.3390/biomedinformatics3040064 - 8 Nov 2023
Cited by 1 | Viewed by 859
Abstract
Precision medicine approaches often rely on complex and integrative analyses of multiple biomarkers from “omics” data to generate insights that can help with either diagnostic, prognostic, or therapeutical decisions. Such insights are often made using machine learning (ML) models that perform sample classification [...] Read more.
Precision medicine approaches often rely on complex and integrative analyses of multiple biomarkers from “omics” data to generate insights that can help with either diagnostic, prognostic, or therapeutical decisions. Such insights are often made using machine learning (ML) models that perform sample classification for a particular phenotype (yes/no). Building such models is a challenge and time-consuming, requiring advanced coding skills and mathematical modelling expertise. Artificial intelligence (AI) is a methodological solution that has the potential to facilitate, optimize, and scale model development. In this work, we developed an AI-based, user-friendly, and code-free platform that fully automated the development of predictive models from quantitative “omics” data. Here, we show the application of this tool with the development of cancer survival prognostics models using real-life data from breast, lung, and renal cancer transcriptomes. In comparison to other models, our generated models rendered performances with competitive sensitivities (72–85%), specificities (76–85%), accuracies (75–85%), and Receiver Operating Characteristic curves with superior Areas Under the Curve (ROC-AUC of 77–86%). Further, we reported the associated sets of genes (biomarkers) and their expression patterns that were predictive of cancer survival. Moreover, we made our models available as online tools to generate prognostic predictions based on the gene expressions of the biomarkers. In conclusion, we demonstrated that our tool is a robust, user-friendly solution for developing bespoke predictive tools from “omics” data, which facilitate precision medicine applications to the point-of-care. Full article
(This article belongs to the Special Issue Application of Semantic Web Technologies in Biomedicine and Biomedical Informatics)
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