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Artificial Intelligence and Machine Learning for Biomedical Applications: Entropy and Information-Theoretic Perspectives

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Entropy and Biology".

Deadline for manuscript submissions: 15 January 2027 | Viewed by 1982

Editor

Dr. Liang Dong

E-Mail Website
Guest Editor
1. Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA
2. Radiology, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
Interests: artificial intelligence/machine learning with biomedical applications; edge AI and hardware acceleration; information theory; signal processing and communications; cyber–physical system and security

Special Issue Information

Dear Colleagues,

The rapid expansion of biomedical imaging, wearable sensing, multi-omics profiling, and electronic health records has generated large-scale, heterogeneous, and complex datasets that challenge traditional analytical approaches. Recent advances in artificial intelligence and machine learning—spanning foundation models, multimodal learning, graph neural networks, generative AI, and reinforcement learning—are reshaping what is possible in biomedical discovery, disease characterization, and precision medicine. These developments open new opportunities for understanding biological systems, identifying biomarkers, predicting treatment responses, and supporting reliable clinical decision-making.

This Special Issue brings together cutting-edge research at the intersection of AI, machine learning, and biomedical science. We welcome contributions involving novel algorithms, theoretical advances, and real-world biomedical applications. Topics of interest include, but are not limited to, deep learning for imaging and radiomics; multimodal integration of omics and clinical data; graph and network modeling of biological systems; generative and self-supervised approaches; uncertainty quantification and interpretability; disease progression modeling; and digital health analytics.

To align with the journal’s scope, submissions incorporating entropy-based metrics, complexity analysis, or information-theoretic concepts are especially encouraged—whether applied to representation learning, multimodal fusion, uncertainty estimation, or the characterization of information flow and structure in biological systems. This Special Issue aims to highlight emerging methodologies and inspire new directions for robust, trustworthy, and impactful biomedical AI.

Dr. Liang Dong
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly 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
  • medical imaging and radiomics
  • multi-omics and precision medicine
  • graph neural networks and network biology
  • generative and self-supervised learning
  • multimodal data fusion
  • uncertainty quantification and interpretability
  • entropy and information-theoretic methods
  • complexity analysis in biological systems

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Published Papers (2 papers)

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Research

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24 pages, 2468 KB  
Article
Exploring Neurofunctional Phase Transition Patterns in Autism Spectrum Disorder via Thermodynamics Parameters
by Dayu Qin, Yuzhe Chen and Ercan E. Kuruoglu
Entropy 2026, 28(5), 567; https://doi.org/10.3390/e28050567 - 19 May 2026
Cited by 1 | Viewed by 348
Abstract
Designing informative descriptors for time-varying complex networks is important for characterizing structural reconfiguration in evolving graph data. This paper introduces a thermodynamics-inspired framework for dynamic graph analysis, centered on Spectral Core Entropy (SCE), node energy, internal energy, and a temperature-like reconfiguration index. These [...] Read more.
Designing informative descriptors for time-varying complex networks is important for characterizing structural reconfiguration in evolving graph data. This paper introduces a thermodynamics-inspired framework for dynamic graph analysis, centered on Spectral Core Entropy (SCE), node energy, internal energy, and a temperature-like reconfiguration index. These quantities provide a compact representation of how graph organization changes over time. We apply this framework to resting-state fMRI data from autism spectrum disorder (ASD) and control subjects. At the event level, the temperature index shows a statistically significant but modest association with low-SSIM reconfiguration events, indicating that it serves as a weak yet reproducible marker of rapid network change. On controlled synthetic dynamic graphs, the framework exhibits regime-dependent sensitivity: spectral-core change is more informative under rewiring, whereas the temperature index is more informative under gain modulation. At the node level, node energy highlights regional differences between ASD and control groups, providing interpretable neuroscientific context for dynamic brain connectivity. Overall, the proposed framework provides a promising and computationally tractable approach for characterizing reconfiguration patterns in dynamic brain networks and other evolving complex systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Biomedical Applications: Entropy and Information-Theoretic Perspectives)
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Review

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38 pages, 585 KB  
Review
A Unified Information Bottleneck Framework for Multimodal Biomedical Machine Learning
by Liang Dong
Entropy 2026, 28(4), 445; https://doi.org/10.3390/e28040445 - 14 Apr 2026
Viewed by 1172
Abstract
Multimodal biomedical machine learning increasingly integrates heterogeneous data sources (including medical imaging, multi-omics profiles, electronic health records, and wearable sensor signals) to support clinical diagnosis, prognosis, and treatment response prediction. Despite strong empirical performance, most existing multimodal systems lack a principled theoretical foundation [...] Read more.
Multimodal biomedical machine learning increasingly integrates heterogeneous data sources (including medical imaging, multi-omics profiles, electronic health records, and wearable sensor signals) to support clinical diagnosis, prognosis, and treatment response prediction. Despite strong empirical performance, most existing multimodal systems lack a principled theoretical foundation for understanding why fusion improves prediction, how information is distributed across modalities, and when models can be trusted under incomplete or shifting data. This paper develops a unified information-theoretic framework that formalizes multimodal biomedical learning as an information optimization problem. We formulate multimodal representation learning through the information bottleneck principle, deriving a variational objective that balances predictive sufficiency against informational compression in an architecture-agnostic manner. Building on this foundation, we introduce information-theoretic tools for decomposing modality contributions via conditional mutual information, quantifying redundancy and synergy, and diagnosing fusion collapse. We further show that robustness to missing modalities can be cast as an information consistency problem and extend the framework to longitudinal disease modeling through transfer entropy and sequential information bottleneck objectives. Applications to multimodal foundation models, uncertainty quantification, calibration, and out-of-distribution detection are developed. Empirical case studies across three biomedical datasets (TCGA breast cancer multi-omics, TCGA glioma clinical-plus-molecular data, and OASIS-2 longitudinal Alzheimer’s data) show that the framework’s key quantities are computable and interpretable on real data: MI decomposition identifies modality dominance and redundancy; the VMIB traces a compression–prediction tradeoff in the information plane; entropy-based selective prediction raises accuracy from 0.787 to 0.939 at 50% coverage; transfer entropy reveals stage-dependent modality influence in disease progression; and pretraining/adaptation diagnostics distinguish efficient from wasteful fine-tuning strategies. Together, these results develop entropy and mutual information as organizing principles for the design, analysis, and evaluation of multimodal biomedical AI systems. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning for Biomedical Applications: Entropy and Information-Theoretic Perspectives)
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