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Advances and Applications of Machine Learning for Bioinformatics

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

Deadline for manuscript submissions: 20 February 2026 | Viewed by 776

Special Issue Editor

Special Issue Information

Dear Colleagues,

The Special Issue "Advances in Machine Learning for Bioinformatics" aims to bring together cutting-edge research that highlights the applications, challenges, and opportunities of machine learning in bioinformatics. As the field of bioinformatics continues to expand, machine learning techniques offer powerful tools to analyze complex biological data, identify patterns, and derive meaningful insights. This Special Issue invites contributions on diverse topics, including but not limited to, genomics, proteomics, systems biology, computational biology, and healthcare. Emphasis will be given to novel methodologies, algorithms, and case studies that demonstrate the effectiveness of machine learning in solving critical problems in biological sciences.

Prof. Dr. Malik Yousef
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.

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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

  • machine learning
  • bioinformatics
  • genomics
  • proteomics
  • computational biology
  • systems biology
  • deep learning
  • biological data analysis
  • healthcare informatics
  • predictive modeling

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Published Papers (1 paper)

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Research

24 pages, 1990 KiB  
Article
Evaluating Skin Tone Fairness in Convolutional Neural Networks for the Classification of Diabetic Foot Ulcers
by Sara Seabra Reis, Luis Pinto-Coelho, Maria Carolina Sousa, Mariana Neto, Marta Silva and Miguela Sequeira
Appl. Sci. 2025, 15(15), 8321; https://doi.org/10.3390/app15158321 (registering DOI) - 26 Jul 2025
Abstract
The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical [...] Read more.
The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations. Full article
(This article belongs to the Special Issue Advances and Applications of Machine Learning for Bioinformatics)
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