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Mathematics
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Mathematical Approaches to Advanced Applications in Biomedicine Using Machine Learning
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Mathematical Approaches to Advanced Applications in Biomedicine Using Machine Learning

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 31 October 2025 | Viewed by 1045

Special Issue Editor

Dr. Mehmet Eren Ahsen

E-Mail Website
Guest Editor
1. Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, IL, USA
2. Department of Biomedical and Translational Sciences, Carle Illinois School of Medicine, Urbana, IL, USA
Interests: AI; deep learning; medical decision-making; computational biology

Special Issue Information

Dear Colleagues,

Machine learning methods have become increasingly powerful in recent years and transformed various domains, and biomedicine is no exception. ML has found numerous applications in the field of biomedicine, tackling complex problems such as predicting 3D structures of proteins, deciphering intricate genetic regulatory networks, and assisting in medical decisions, such as diagnosing diseases and recommending personalized treatment plans. These applications highlight the potential of ML to revolutionize and significantly enhance various facets of biomedicine. With great enthusiasm, we would therefore like to announce our forthcoming Special Issue, named "Mathematical Approaches to Advanced Applications in Biomedicine Using Machine Learning".

Given the interdisciplinary nature of the Special Issue, we invite researchers from different areas of mathematics, statistics, computer science, biology, and medicine to contribute. Topics include but are not limited to:

  • Novel machine learning algorithms for analyzing complex biological data such as genetics, genomics, and proteomics;
  • ML algorithms for the integration of multimodal data in bioinformatics;
  • Machine learning-driven drug discovery and development;
  • Disease diagnosis, prognosis, and treatment recommendations using AI;
  • Applications of ML methods to clinical decisions such as disease diagnosis and treatment recommendations;
  • Mathematical modelling of ethical considerations and challenges in using machine learning in biomedicine.

Dr. Mehmet Eren Ahsen
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. 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.

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

Published Papers (1 paper)

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Review

22 pages, 437 KiB  
Review
Harnessing Unsupervised Ensemble Learning for Biomedical Applications: A Review of Methods and Advances
by Mehmet Eren Ahsen
Mathematics 2025, 13(3), 420; https://doi.org/10.3390/math13030420 - 27 Jan 2025
Viewed by 710
Abstract
Advancements in data availability and computational techniques, including machine learning, have transformed the field of bioinformatics, enabling the robust analysis of complex, high-dimensional, and heterogeneous biomedical data. This paper explores how diverse bioinformatics tasks, including differential expression analysis, network inference, and somatic mutation [...] Read more.
Advancements in data availability and computational techniques, including machine learning, have transformed the field of bioinformatics, enabling the robust analysis of complex, high-dimensional, and heterogeneous biomedical data. This paper explores how diverse bioinformatics tasks, including differential expression analysis, network inference, and somatic mutation calling, can be reframed as binary classification tasks, thereby providing a unifying framework for their analysis. Traditional single-method approaches often fail to generalize across datasets due to differences in data distributions, noise levels, and underlying biological contexts. Ensemble learning, particularly unsupervised ensemble approaches, emerges as a compelling solution by integrating predictions from multiple algorithms to leverage their strengths and mitigate weaknesses. This review focuses on the principles and recent advancements in ensemble learning, with a particular emphasis on unsupervised ensemble methods. These approaches demonstrate their ability to address critical challenges in bioinformatics, such as the lack of labeled data and the integration of predictions from algorithms operating on different scales. Overall, this paper highlights the transformative potential of ensemble learning in advancing predictive accuracy, robustness, and interpretability across diverse bioinformatics applications. Full article
(This article belongs to the Special Issue Mathematical Approaches to Advanced Applications in Biomedicine Using Machine Learning)
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