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Applied Sciences
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Bioinformatics in Healthcare to Prevent Cancer and Children Obesity
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Bioinformatics in Healthcare to Prevent Cancer and Children Obesity

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Biomedical Engineering".

Deadline for manuscript submissions: 20 December 2024 | Viewed by 2091

Special Issue Editors

Dr. Athanasios Anastasiou

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Guest Editor
Biomedical Engineering Laboratory, School of Electrical Engineering, National Technical University of Athens, 10682 Athens, Greece
Interests: M-health; E-health; biomedical engineering; software engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Dimitrios Dionisios Koutsouris

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Guest Editor
Division of Information Transmission Systems and Material Technology, National Technical University of Athens, 10682 Athens, Greece
Interests: biomedical signal processing; clinical engineering; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The field of bioinformatics has various applications in medicine, ranging from research into genes to the use of drugs for prevention. Firstly, bioinformatics has many uses in pharmaceuticals: bioinformatics researchers have played essential roles in pharmaceutical research, especially for the treatment of infectious diseases. Moreover, bioinformatics has also innovated personalized medicine research, thus assisting in bringing new discoveries in terms of drugs that can be personalized to patient genetic patterns. Secondly, this research has a variety of uses in prevention: just like pharmaceuticals, bioinformatics can be combined with epidemiology to create preventive medicine by determining the causes of health issues, community healthcare infrastructure, disease patterns, etc. Finally, these treatments have extensive uses in therapy: bioinformatics can also be useful in gene therapy, especially for individual genes that have been adversely affected. This application of bioinformatics has been researched by genetic scientists who have found that someone’s genetic profile can be improved with the help of bioinformatics. In this SI we will seek to cover:

  • AI in the healthcare;
  • Bioinformatics in healthcare;
  • Prevention of cancer using AI and image processing;
  • Detection and prevention of obesity in children.

Dr. Athanasios Anastasiou
Prof. Dimitrios-Dionissios Koutsouris
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.

 

Keywords

  • image processing
  • bioinformatics
  • AI
  • children obesity
  • cancer prevention

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Published Papers (2 papers)

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Research

16 pages, 3278 KiB  
Article
Integrating Shapley Values into Machine Learning Techniques for Enhanced Predictions of Hospital Admissions
by Georgios Feretzakis, Aikaterini Sakagianni, Athanasios Anastasiou, Ioanna Kapogianni, Effrosyni Bazakidou, Petros Koufopoulos, Yiannis Koumpouros, Christina Koufopoulou, Vasileios Kaldis and Vassilios S. Verykios
Appl. Sci. 2024, 14(13), 5925; https://doi.org/10.3390/app14135925 - 7 Jul 2024
Viewed by 779
Abstract
(1) Background: Predictive modeling is becoming increasingly relevant in healthcare, aiding in clinical decision making and improving patient outcomes. However, many of the most potent predictive models, such as deep learning algorithms, are inherently opaque, and their decisions are challenging to interpret. This [...] Read more.
(1) Background: Predictive modeling is becoming increasingly relevant in healthcare, aiding in clinical decision making and improving patient outcomes. However, many of the most potent predictive models, such as deep learning algorithms, are inherently opaque, and their decisions are challenging to interpret. This study addresses this challenge by employing Shapley Additive Explanations (SHAP) to facilitate model interpretability while maintaining prediction accuracy. (2) Methods: We utilized Gradient Boosting Machines (GBMs) to predict patient outcomes in an emergency department setting, with a focus on model transparency to ensure actionable insights. (3) Results: Our analysis identifies “Acuity”, “Hours”, and “Age” as critical predictive features. We provide a detailed exploration of their intricate interactions and effects on the model’s predictions. The SHAP summary plots highlight that “Acuity” has the highest impact on predictions, followed by “Hours” and “Age”. Dependence plots further reveal that higher acuity levels and longer hours are associated with poorer patient outcomes, while age shows a non-linear relationship with outcomes. Additionally, SHAP interaction values uncover that the interaction between “Acuity” and “Hours” significantly influences predictions. (4) Conclusions: We employed force plots for individual-level interpretation, aligning with the current shift toward personalized medicine. This research highlights the potential of combining machine learning’s predictive power with interpretability, providing a promising route concerning a data-driven, evidence-based healthcare future. Full article
(This article belongs to the Special Issue Bioinformatics in Healthcare to Prevent Cancer and Children Obesity)
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11 pages, 1566 KiB  
Article
Texture Analysis in [18F]-Fluciclovine PET/CT Aids to Detect Prostate Cancer Biochemical Relapse: Report of a Preliminary Experience
by Laura Travascio, Sara De Novellis, Piera Turano, Angelo Domenico Di Nicola, Vincenzo Di Egidio, Ferdinando Calabria, Luca Frontino, Viviana Frantellizzi, Giuseppe De Vincentis, Andrea Cimini and Maria Ricci
Appl. Sci. 2024, 14(8), 3469; https://doi.org/10.3390/app14083469 - 19 Apr 2024
Viewed by 725
Abstract
Background. As artificial intelligence is expanding its applications in medicine, metabolic imaging is gaining the ability to retrieve data otherwise missed by even an experienced naked eye. Also, new radiopharmaceuticals and peptides aim to increase the specificity of positron emission tomography (PET) scans. [...] Read more.
Background. As artificial intelligence is expanding its applications in medicine, metabolic imaging is gaining the ability to retrieve data otherwise missed by even an experienced naked eye. Also, new radiopharmaceuticals and peptides aim to increase the specificity of positron emission tomography (PET) scans. Herein, a preliminary experience is reported regarding searching for a texture signature in routinely performed [F18]Fluciclovine imaging in prostate cancer. Materials and methods. Twenty-nine patients who underwent a PET/computed tomography (CT) scan with [18F]Fluciclovine because of biochemical prostate cancer relapse were retrospectively enrolled. First- and second-order radiomic features were manually extracted in lesions visually considered pathologic from the Local Image Features Extraction (LIFEx) platform. Statistical analysis was performed on a database of 29 lesions, one1 per patient. The dataset was split to have 20 lesions for the model training set and 9 lesions for the validation set. The Wilcoxon–Mann–Whitney test was used on the training set to select the most significant features (p-value < 0.05) predicting the dichotomous outcome in a univariate analysis. Results. The best model for predicting the outcome was found to be a multiple logistic linear regression model with two features as variables: an intensity histogram type and a gray-level size zone-based type. Conclusions. Texture analysis of [F18]Fluciclovine PET scans helps in defining prostate cancer relapse in a daily clinical setting. Full article
(This article belongs to the Special Issue Bioinformatics in Healthcare to Prevent Cancer and Children Obesity)
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