Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
BioMedInformatics
Special Issues
Editor's Choices Series for Methods in Biomedical Informatics Section
share announcement

Editor's Choices Series for Methods in Biomedical Informatics Section

A special issue of BioMedInformatics (ISSN 2673-7426). This special issue belongs to the section "Methods in Biomedical Informatics".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1092

Special Issue Editor

Prof. Dr. Rosalba Giugno

E-Mail Website
Guest Editor
LS2_10—Bioinformatics, Università degli Studi di Verona, 37129 Verona, Italy
Interests: bioinformatics; computational biology; medical imaging analysis; artificial intelligence; machine learning; data analysis; personalized medicine; predictive modeling; healthcare innovation; methodological advancements
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Editor's Choice Series for Methods in Biomedical Informatics Section presents an insightful compilation of cutting-edge methodologies pivotal in the convergence of biomedical science and informatics. This curated selection encompasses an array of innovative techniques, tools, and approaches instrumental in advancing biomedical research, clinical practice, and healthcare innovation.

Spanning diverse domains such as bioinformatics, computational biology, medical imaging analysis, artificial intelligence, and machine learning, this series delves into the methodologies reshaping the landscape of informatics in healthcare. From sophisticated algorithms for data analysis to pioneering strategies in personalized medicine and predictive modeling, this collection encapsulates the dynamic evolution of the methods driving transformative impacts.

Please note that this series does not accept submissions of brief reports, but focuses on the in-depth exploration and analysis of methodologies and their application in the biomedical informatics domain.

Prof. Dr. Rosalba Giugno
Guest Editor

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

  • bioinformatics
  • computational biology
  • medical imaging analysis
  • artificial intelligence
  • machine learning
  • data analysis
  • personalized medicine
  • predictive modeling
  • healthcare innovation
  • methodological advancements

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 2325 KiB  
Article
Machine Learning in Allergic Contact Dermatitis: Identifying (Dis)similarities between Polysensitized and Monosensitized Patients
by Aikaterini Kyritsi, Anna Tagka, Alexander Stratigos and Vangelis D. Karalis
BioMedInformatics 2024, 4(2), 1348-1362; https://doi.org/10.3390/biomedinformatics4020074 - 17 May 2024
Abstract
Background: Allergic contact dermatitis (ACD) is a delayed hypersensitivity reaction occurring in sensitized individuals due to exposure to allergens. Polysensitization, defined as positive reactions to multiple unrelated haptens, increases the risk of ACD development and affects patients’ quality of life. The aim of [...] Read more.
Background: Allergic contact dermatitis (ACD) is a delayed hypersensitivity reaction occurring in sensitized individuals due to exposure to allergens. Polysensitization, defined as positive reactions to multiple unrelated haptens, increases the risk of ACD development and affects patients’ quality of life. The aim of this study is to apply machine learning in order to analyze the association between ACD, polysensitization, individual susceptibility, and patients’ characteristics. Methods: Patch test results and demographics from 400 ACD patients (Study protocol Nr. 3765/2022), categorized as polysensitized or monosensitized, were analyzed. Classic statistical analysis and multiple correspondence analysis (MCA) were utilized to explore relationships among variables. Results: The findings revealed significant associations between patient characteristics and ACD patterns, with hand dermatitis showing the strongest correlation. MCA provided insights into the complex interplay of demographic and clinical factors influencing ACD prevalence. Conclusion: Overall, this study highlights the potential of machine learning in unveiling hidden patterns within dermatological data, paving the way for future advancements in the field. Full article
(This article belongs to the Special Issue Editor's Choices Series for Methods in Biomedical Informatics Section)
►▼ Show Figures

Figure 1

13 pages, 2093 KiB  
Article
A Smartphone-Based Algorithm for L Test Subtask Segmentation
by Alexis L. McCreath Frangakis, Edward D. Lemaire and Natalie Baddour
BioMedInformatics 2024, 4(2), 1262-1274; https://doi.org/10.3390/biomedinformatics4020069 - 10 May 2024
Viewed by 318
Abstract
Background: Subtask segmentation can provide useful information from clinical tests, allowing clinicians to better assess a patient’s mobility status. A new smartphone-based algorithm was developed to segment the L Test of functional mobility into stand-up, sit-down, and turn subtasks. Methods: Twenty-one able-bodied participants [...] Read more.
Background: Subtask segmentation can provide useful information from clinical tests, allowing clinicians to better assess a patient’s mobility status. A new smartphone-based algorithm was developed to segment the L Test of functional mobility into stand-up, sit-down, and turn subtasks. Methods: Twenty-one able-bodied participants each completed five L Test trials, with a smartphone attached to their posterior pelvis. The smartphone used a custom-designed application that collected linear acceleration, gyroscope, and magnetometer data, which were then put into a threshold-based algorithm for subtask segmentation. Results: The algorithm produced good results (>97% accuracy, >98% specificity, >74% sensitivity) for all subtasks. Conclusions: These results were a substantial improvement compared with previously published results for the L Test, as well as similar functional mobility tests. This smartphone-based approach is an accessible method for providing useful metrics from the L Test that can lead to better clinical decision-making. Full article
(This article belongs to the Special Issue Editor's Choices Series for Methods in Biomedical Informatics Section)
►▼ Show Figures

Figure 1

11 pages, 3190 KiB  
Article
Assaying and Classifying T Cell Function by Cell Morphology
by Xin Wang, Stacey M. Fernandes, Jennifer R. Brown and Lance C. Kam
BioMedInformatics 2024, 4(2), 1144-1154; https://doi.org/10.3390/biomedinformatics4020063 - 26 Apr 2024
Viewed by 473
Abstract
Immune cell function varies tremendously between individuals, posing a major challenge to emerging cellular immunotherapies. This report pursues the use of cell morphology as an indicator of high-level T cell function. Short-term spreading of T cells on planar, elastic surfaces was quantified by [...] Read more.
Immune cell function varies tremendously between individuals, posing a major challenge to emerging cellular immunotherapies. This report pursues the use of cell morphology as an indicator of high-level T cell function. Short-term spreading of T cells on planar, elastic surfaces was quantified by 11 morphological parameters and analyzed to identify effects of both intrinsic and extrinsic factors. Our findings identified morphological features that varied between T cells isolated from healthy donors and those from patients being treated for Chronic Lymphocytic Leukemia (CLL). This approach also identified differences between cell responses to substrates of different elastic modulus. Combining multiple features through a machine learning approach such as Decision Tree or Random Forest provided an effective means for identifying whether T cells came from healthy or CLL donors. Further development of this approach could lead to a rapid assay of T cell function to guide cellular immunotherapy. Full article
(This article belongs to the Special Issue Editor's Choices Series for Methods in Biomedical Informatics Section)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop