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BioMedInformatics
Special Issues
Editor's Choices Series for Methods in Biomedical Informatics Section
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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 3624

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

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

34 pages, 3635 KiB  
Article
Machine Learning for Extraction of Image Features Associated with Progression of Geographic Atrophy
by Janan Arslan and Kurt Benke
BioMedInformatics 2024, 4(3), 1638-1671; https://doi.org/10.3390/biomedinformatics4030089 - 2 Jul 2024
Viewed by 485
Abstract
Background: Several studies have investigated various features and models in order to understand the growth and progression of the ocular disease geographic atrophy (GA). Commonly assessed features include age, sex, smoking, alcohol consumption, sedentary lifestyle, hypertension, and diabetes. There have been inconsistencies regarding [...] Read more.
Background: Several studies have investigated various features and models in order to understand the growth and progression of the ocular disease geographic atrophy (GA). Commonly assessed features include age, sex, smoking, alcohol consumption, sedentary lifestyle, hypertension, and diabetes. There have been inconsistencies regarding which features correlate with GA progression. Chief amongst these inconsistencies is whether the investigated features are readily available for analysis across various ophthalmic institutions. Methods:In this study, we focused our attention on the association of fundus autofluorescence (FAF) imaging features and GA progression. Our method included feature extraction using radiomic processes and feature ranking by machine learning incorporating the algorithm XGBoost to determine the best-ranked features. This led to the development of an image-based linear mixed-effects model, which was designed to account for slope change based on within-subject variability and inter-eye correlation. Metrics used to assess the linear mixed-effects model included marginal and conditional R2, Pearson’s correlation coefficient (r), root mean square error (RMSE), mean error (ME), mean absolute error (MAE), mean absolute deviation (MAD), the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and loglikelihood. Results: We developed a linear mixed-effects model with 15 image-based features. The model results were as follows: R2 = 0.96, r = 0.981, RMSE = 1.32, ME = −7.3 × 10−15, MAE = 0.94, MAD = 0.999, AIC = 2084.93, BIC = 2169.97, and log likelihood = −1022.46. Conclusions: The advantage of our method is that it relies on the inherent properties of the image itself, rather than the availability of clinical or demographic data. Thus, the image features discovered in this study are universally and readily available across the board. Full article
(This article belongs to the Special Issue Editor's Choices Series for Methods in Biomedical Informatics Section)
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12 pages, 514 KiB  
Article
Calibrating Glucose Sensors at the Edge: A Stress Generation Model for Tiny ML Drift Compensation
by Anna Sabatini, Costanza Cenerini, Luca Vollero and Danilo Pau
BioMedInformatics 2024, 4(2), 1519-1530; https://doi.org/10.3390/biomedinformatics4020083 - 9 Jun 2024
Viewed by 440
Abstract
Background: Continuous glucose monitoring (CGM) systems offer the advantage of noninvasive monitoring and continuous data on glucose fluctuations. This study introduces a new model that enables the generation of synthetic but realistic databases that integrate physiological variables and sensor attributes into a [...] Read more.
Background: Continuous glucose monitoring (CGM) systems offer the advantage of noninvasive monitoring and continuous data on glucose fluctuations. This study introduces a new model that enables the generation of synthetic but realistic databases that integrate physiological variables and sensor attributes into a dataset generation model and this, in turn, enables the design of improved CGM systems. Methods: The presented approach uses a combination of physiological data and sensor characteristics to construct a model that considers the impact of these variables on the accuracy of CGM measures. A dataset of 500 sensor responses over a 15-day period is generated and analyzed using machine learning algorithms (random forest regressor and support vector regressor). Results: The random forest and support vector regression models achieved Mean Absolute Errors (MAEs) of 16.13 mg/dL and 16.22 mg/dL, respectively. In contrast, models trained solely on single sensor outputs recorded an average MAE of 11.01±5.12 mg/dL. These findings demonstrate the variable impact of integrating multiple data sources on the predictive accuracy of CGM systems, as well as the complexity of the dataset. Conclusions: This approach provides a foundation for developing more precise algorithms and introduces its initial application of Tiny Machine Control Units (MCUs). More research is recommended to refine these models and validate their effectiveness in clinical settings. Full article
(This article belongs to the Special Issue Editor's Choices Series for Methods in Biomedical Informatics Section)
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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
Viewed by 675
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)
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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
Cited by 1 | Viewed by 736
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)
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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 963
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)
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