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Bioengineering
Special Issues
Biomedical Data Mining: Emerging Methods and Applications
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Biomedical Data Mining: Emerging Methods and Applications

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 483

Special Issue Editors

Dr. Andrei Flavius Radu

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Guest Editor
1. Department of Psycho-Neurosciences and Recovery, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania
2. Doctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, Romania
Interests: toxicology; xenobiotics; in vivo studies; public health; in vitro studies; pharmacovigilance
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Delia Mirela Tit

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Guest Editor
1. Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, 410028 Oradea, Romania
2. Doctoral School of Biomedical Sciences, University of Oradea, 410087 Oradea, Romania
Interests: digital health; natural language processing; predictive modeling; text mining; research mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid growth of biomedical data, from electronic health records and clinical registries to multi-omics profiles, imaging, wearable sensors, digital phenotyping, and scientific literature assessment, offers unprecedented opportunities to improve prevention, diagnosis, and treatment. However, these data are often high-dimensional, heterogeneous, incomplete, difficult to access, and subject to bias, making robust analytical methods essential for generating clinically meaningful insights.

This Special Issue aims to consolidate, update, and advance the current state of knowledge in biomedical data mining through a comprehensive spectrum of studies, ranging from algorithmic development and computational methodologies to practical implementations in healthcare settings. Furthermore, it intends to address critical research gaps by enhancing the accuracy, efficiency, and interpretability of data-driven approaches in biomedicine.

Topics of interest include, but are not limited to, machine learning and deep learning in healthcare, data safety, natural language processing to mine unstructured data, network and graph-based analysis, multimodal data integration, bibliometric and scientometric analyses, feature engineering and explainable AI, federated learning and privacy-preserving analytics, and reproducibility and bias mitigation. We also encourage the submission of reviews and systematic perspectives that summarize emerging trends and methodological challenges.

Dr. Andrei Flavius Radu
Prof. Dr. Delia Mirela Tit
Guest Editors

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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering 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 2700 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 mining
  • multimodal data integration
  • explainable AI
  • privacy-preserving analytics
  • network and graph-based analysis

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Published Papers (1 paper)

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Research

17 pages, 616 KB  
Article
Cross-Lingual Alzheimer’s Disease Speech Detection: Polarity Inversion and Few-Shot Calibration Strategies
by Qingyi Wang and Meihong Wu
Bioengineering 2026, 13(6), 629; https://doi.org/10.3390/bioengineering13060629 - 27 May 2026
Viewed by 109
Abstract
Speech-based non-invasive screening offers a cost-effective and scalable approach for the early detection of Alzheimer’s disease (AD). However, the clinical utility of deep learning models remains severely constrained by the scarcity of labeled speech data in low-resource languages, necessitating cross-lingual transfer learning. Conventional [...] Read more.
Speech-based non-invasive screening offers a cost-effective and scalable approach for the early detection of Alzheimer’s disease (AD). However, the clinical utility of deep learning models remains severely constrained by the scarcity of labeled speech data in low-resource languages, necessitating cross-lingual transfer learning. Conventional domain adaptation paradigms typically assume semantically consistent feature domains and focus heavily on aligning marginal distributions; however, they suffer catastrophic performance degradation when applied to cross-lingual pathologic speech. By analyzing disease-associated representation vectors within a self-supervised HuBERT space, we uncover a systematic mechanism driving this failure, a phenomenon we term cross-lingual polarity flip, where the direction of disease-relative-to-control feature offsets fundamentally reverses between languages. While prior multilingual studies have largely discarded such dimensional inconsistencies as ungeneralizable noise, a 500-round Monte Carlo stability analysis demonstrates that these flips occur in a highly stable, structural manner across 18.3% of top discriminative dimensions. Leveraging this insight, we introduce Monte Carlo Polarity Flip Calibration (MC-PFC), a few-shot framework designed to explicitly rectify flip orientations. Requiring only five labeled support samples per class from the target domain, MC-PFC robustly estimates direction flips via a separability-weighted ensemble voting mechanism. Evaluated on a strictly held-out Chinese blind test set, MC-PFC achieves an area under the receiver operating characteristic curve (AUC) of 0.871, recovering 99.5% of the performance achieved by a full in-domain trained upper bound (AUC = 0.875). Ablation experiments confirm that direction calibration yields a substantial +0.361 AUC gain, vastly outperforming standard distribution alignment (+0.081). This work establishes a data-efficient paradigm for cross-lingual medical analysis, shifting the clinical AI focus from discarding cross-lingual discrepancies to actively modeling and calibrating them. Full article
(This article belongs to the Special Issue Biomedical Data Mining: Emerging Methods and Applications)
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