Advanced Mathematical Methods for Machine Learning in Biomedical Applications
A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 763
Special Issue Editors
Interests: machine learning; bioinformatics; graph learning; multi-modal information fusion
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; medical image analysis; deep learning; continual learning; few-shot learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of machine learning (ML) into biomedical research is transforming our capacity to decode complex biological systems and enhance healthcare outcomes. However, the unique characteristics of biomedical data—such as high dimensionality, heterogeneity, and inherent noise—pose substantial challenges that require sophisticated mathematical approaches. This Special Issue seeks to bridge this gap by emphasizing the pivotal role of advanced mathematical methods in driving the next generation of biomedical machine learning.
We aim to compile cutting-edge research that develops and applies novel mathematical and computational models to tackle pressing challenges in biomedicine. The scope of this Special Issue is broad, encompassing innovative studies on the development of ML algorithms grounded in rigorous mathematical theory, advanced techniques for multimodal data integration and feature extraction, and novel mathematical frameworks to manage data imbalance and bias, thereby enhancing model robustness and fairness. We invite submissions of original research and comprehensive review articles. By bringing together diverse expertise, this collection will demonstrate how advanced mathematics is fundamental to realizing the full potential of machine learning in addressing complex challenges in biology and medicine.
Dr. Dayu Hu
Dr. Jinghua Zhang
Guest Editors
Manuscript Submission Information
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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. Mathematics 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 2600 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
- biomedical data analysis
- computational modeling
- multimodal learning
- data imbalance
- algorithmic fairness
- graph neural networks
- feature extraction
- mathematical methods in biomedicine
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