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Advances and Applications of Machine Learning for Bioinformatics
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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 3421

Special Issue Editor

Prof. Dr. Malik Yousef

E-Mail Website
Guest Editor
Department of Information Systems, Zefat Academic College, Zefat 13206, Israel
Interests: bioinformatics; text mining; biological domain knowledge-based feature selection on gene expression data; microRNA; one-class classification
Special Issues, Collections and Topics in MDPI journals

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.

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

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

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Published Papers (3 papers)

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Research

20 pages, 2434 KB  
Article
Machine Learning-Based Prediction of Autism Spectrum Disorder and Discovery of Related Metagenomic Biomarkers with Explainable AI
by Mustafa Temiz, Burcu Bakir-Gungor, Nur Sebnem Ersoz and Malik Yousef
Appl. Sci. 2025, 15(16), 9214; https://doi.org/10.3390/app15169214 - 21 Aug 2025
Viewed by 355
Abstract
Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Recent studies have suggested that gut microbiota may play a role in the pathophysiology of ASD. This study aims to develop a classification model for [...] Read more.
Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Recent studies have suggested that gut microbiota may play a role in the pathophysiology of ASD. This study aims to develop a classification model for ASD diagnosis and to identify ASD-associated biomarkers by analyzing metagenomic data at the taxonomic level. Methods: The performances of five different methods were tested in this study. These methods are (i) SVM-RCE, (ii) RCE-IFE, (iii) microBiomeGSM, (iv) different feature selection methods, and (v) a union method. The last method is based on creating a union feature set consisting of the features with importance scores greater than 0.5, identified using the best-performing feature selection methods. Results: In our 10-fold Monte Carlo cross-validation experiments on ASD-associated metagenomic data, the most effective performance metric (an AUC of 0.99) was obtained using the union feature set (17 features) and the AdaBoost classifier. In other words, we achieve superior machine learning performance with a few features. Additionally, the SHAP method, which is an explainable artificial intelligence method, is applied to the union feature set, and Prevotella sp. 109 is identified as the most important microorganism for ASD development. Conclusions: These findings suggest that the proposed method may be a promising approach for uncovering microbial patterns associated with ASD and may inform future research in this area. This study should be regarded as exploratory, based on preliminary findings and hypothesis generation. Full article
(This article belongs to the Special Issue Advances and Applications of Machine Learning for Bioinformatics)
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19 pages, 990 KB  
Article
Machine Learning for Mortality Risk Prediction in Myocardial Infarction: A Clinical-Economic Decision Support Framework
by Konstantinos P. Fourkiotis and Athanasios Tsadiras
Appl. Sci. 2025, 15(16), 9192; https://doi.org/10.3390/app15169192 - 21 Aug 2025
Viewed by 1168
Abstract
Myocardial infarction (MI) remains a leading cause of in-hospital mortality. Early identification of high-risk patients is essential for improving clinical outcomes and optimizing hospital resource allocation. This study presents a machine learning framework for predicting mortality following MI using a publicly available dataset [...] Read more.
Myocardial infarction (MI) remains a leading cause of in-hospital mortality. Early identification of high-risk patients is essential for improving clinical outcomes and optimizing hospital resource allocation. This study presents a machine learning framework for predicting mortality following MI using a publicly available dataset of 1700 patient records, and after excluding records with over 20 missing values and features with more than 300 missing entries, the final dataset included 1547 patients and 113 variables, categorized as binary, categorical, integer, or continuous. Missing values were addressed using denoising autoencoders for continuous features and variational autoencoders for the remaining data. In contrast, feature selection was performed using Random Forest, and PowerTransformer scaling was applied, addressing class imbalance by using SMOTE. Twelve models were evaluated, including Focal-Loss Neural Networks, TabNet, XGBoost, LightGBM, CatBoost, Random Forest, SVM, Logistic Regression, and a voting ensemble. Performance was assessed using multiple metrics, with SVM achieving the highest F1 score (0.6905), ROC-AUC (0.8970), and MCC (0.6464), while Random Forest yielded perfect precision and specificity. To assess generalizability, a subpopulation external validation was conducted by training on male patients and testing on female patients. XGBoost and CatBoost reached the highest ROC-AUC (0.90), while Focal-Loss Neural Network achieved the best MCC (0.53). Overall, the proposed framework outperformed previous studies in key metrics and maintained better performance under demographic shift, supporting its potential for clinical decision-making in post-MI care. Full article
(This article belongs to the Special Issue Advances and Applications of Machine Learning for Bioinformatics)
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24 pages, 1990 KB  
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 - 26 Jul 2025
Viewed by 918
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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