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Algorithms
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Advanced Algorithms for Biomedical Data Analysis
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Advanced Algorithms for Biomedical Data Analysis

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (31 May 2026) | Viewed by 5823

Special Issue Editor

Dr. Micheal Olaolu Arowolo

E-Mail Website
Guest Editor
Department of Public Health Sciences, Health Informatics Program, Xavier University of Louisiana, New Orleans, LA 70461, USA
Interests: bioinformatics; artificial intelligence; machine learning; data science; computing

Special Issue Information

Dear Colleagues,

Advances in genetics, medical imaging, wearable technology, and electronic health records have all contributed to the swift growth of biomedical data, necessitating the development of novel computational techniques for the analysis and interpretation of these complex data. The goal of this Special Issue is to present state-of-the-art research on sophisticated algorithms focused on addressing the unique difficulties of biological data processing. Topics of interest include, but are not restricted to, the following:

  • Deep learning and machine learning models for the diagnosis and prognosis of diseases.
  • Algorithms for analysing proteomic and genomic data.
  • Computational techniques for processing and analysing medical images.
  • Methods of data fusion and integration for multi-omics and multi-modal data.
  • Interpretability and explainable AI in biomedical applications.
  • Large-scale biomedical datasets benefitting from scalable techniques.
  • Clinical text analysis using natural language processing (NLP) applications.

Original research articles, reviews, and case studies showcasing cutting-edge techniques, resources, and applications in this rapidly developing area are encouraged. This Special Issue will showcase the revolutionary potential of sophisticated algorithms in enhancing healthcare outcomes and furthering biomedical research through submissions from scholars and practitioners.

Dr. Micheal Olaolu Arowolo
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms 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 1800 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

  • biomedical data analysis
  • machine learning in healthcare
  • genomic and proteomic data
  • medical image processing
  • explainable AI in biomedicine
  • multi-omics data integration
  • clinical NLP

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Published Papers (5 papers)

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Research

21 pages, 2004 KB  
Article
The Nonlinear Relationship Between Fasting Plasma Glucose, HbA1c, and Blood Pressure: A Cross-Sectional Analysis of 54,881 Adults from NHANES 1999–2023
by Mikhail Kolev, Irina Naskinova, Mariyan Milev, Hristo Kalinov, Gabriela Vasileva and Penko Mitev
Algorithms 2026, 19(5), 369; https://doi.org/10.3390/a19050369 - 7 May 2026
Viewed by 248
Abstract
The relationship between blood glucose levels and blood pressure is well established in clinical literature, yet its precise quantitative characterization, including nonlinear effects, threshold phenomena, and demographic modifiers, remains incompletely understood. In this study, we conducted a comprehensive cross-sectional analysis of the National [...] Read more.
The relationship between blood glucose levels and blood pressure is well established in clinical literature, yet its precise quantitative characterization, including nonlinear effects, threshold phenomena, and demographic modifiers, remains incompletely understood. In this study, we conducted a comprehensive cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES) spanning 11 survey cycles (1999–2023), comprising 54,881 adult participants with at least one glycemic marker and standardized blood pressure measurements. Of these, 26,981 had valid fasting plasma glucose (FPG) measurements, and 49,327 had valid glycated hemoglobin (HbA1c) measurements. We employed restricted cubic splines (RCS), generalized additive models (GAMs), and segmented regression to characterize the dose–response relationship between glycemic markers and both systolic (SBP) and diastolic blood pressure (DBP). A 10 mg/dL increase in FPG was associated with a 0.32 mmHg increase in SBP (95% CI: 0.26–0.38, p < 0.001) after adjusting for age, sex, and body mass index (BMI). Nonlinearity was statistically significant for all exposure–outcome combinations (p < 10−7 for Wald tests). Segmented regression identified a FPG breakpoint at 122.1 mg/dL (95% CI: 119.5–125.6), below which SBP increased at 0.39 mmHg per mg/dL and above which the association was essentially flat. Stratified analyses revealed that the glucose–BP association was strongest in females (β = 0.048 per mg/dL) compared with males (β = 0.021), and in prediabetic individuals (β = 0.065) compared with those with established diabetes (β = 0.014). In the statistical mediation decomposition, body mass index accounted for 23.5% of the total FPG–SBP association. A significant FPG × BMI interaction (p < 0.001) indicated that the glucose–BP relationship is modulated by adiposity. These findings provide a large-scale population-level analysis of the glucose–blood pressure dose–response relationship and identify potential thresholds warranting further investigation for integrated cardiometabolic risk management (95% bootstrap CI: 19.3–28.9%; 1000 resamples); given the cross-sectional design and BMI’s plausible role as a shared upstream determinant of glucose and blood pressure, this proportion is reported as a confounding decomposition rather than as evidence of causal mediation. Insulin resistance (HOMA-IR) and C-reactive protein did not contribute significantly as additional decomposition pathways. Full article
(This article belongs to the Special Issue Advanced Algorithms for Biomedical Data Analysis)
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23 pages, 607 KB  
Article
Stroke Rehabilitation in Virtual Reality Through Enhanced Plantar Pressure Detection Using Sensor Resolution and Adaptive Thresholding
by Audrey Rah and Yuhua Chen
Algorithms 2026, 19(5), 368; https://doi.org/10.3390/a19050368 - 6 May 2026
Viewed by 305
Abstract
Early-stage stroke rehabilitation increasingly incorporates virtual reality (VR) systems to provide interactive motor training and positive reinforcement. However, the minimal voluntary plantar pressure activations generated during early recovery are often below the detection limits of conventional pressure-sensing platforms, restricting timely feedback. This study [...] Read more.
Early-stage stroke rehabilitation increasingly incorporates virtual reality (VR) systems to provide interactive motor training and positive reinforcement. However, the minimal voluntary plantar pressure activations generated during early recovery are often below the detection limits of conventional pressure-sensing platforms, restricting timely feedback. This study quantitatively evaluates the detectability of low-amplitude plantar micro-intent signals under varying sensor resolution and adaptive threshold conditions. Publicly available plantar pressure recordings from the PhysioNet Center for Verification and Evaluation of Stroke (CVES) database were used as physiological baseline signals. Micro-intent was modeled as short-duration half-sine pressure pulses with systematically varied amplitudes and integrated into low-load baseline segments. Sensor resolution was represented through controlled noise modeling to emulate low-, medium-, and high-resolution sensing scenarios. A sliding-window adaptive threshold detector was evaluated across multiple amplitudes and sensitivity stages. The detection probability, false positive rate, and minimum detectable amplitude (defined as ≥80% detection probability) were quantified. The results show that detection probability increases with signal amplitude and shifts toward lower amplitudes with improved sensor resolution and more sensitive threshold configurations. Higher-resolution sensing reduced the minimum detectable amplitude, while adaptive thresholding enabled earlier detection of weak plantar activations without substantial increases in false positives. These findings provide quantitative design guidance for pressure-sensing VR rehabilitation systems targeting early-stage motor recovery. Full article
(This article belongs to the Special Issue Advanced Algorithms for Biomedical Data Analysis)
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21 pages, 526 KB  
Article
Understanding Tradeoffs in Clinical Text Extraction: Prompting, Retrieval-Augmented Generation, and Supervised Learning on Electronic Health Records
by Tanya Yadav, Aditya Tekale, Jeff Chong and Mohammad Masum
Algorithms 2026, 19(3), 215; https://doi.org/10.3390/a19030215 - 13 Mar 2026
Viewed by 727
Abstract
Clinical discharge summaries contain rich patient information but remain difficult to convert into structured representations for downstream analysis. Recent advances in large language models (LLMs) have introduced new approaches for clinical text extraction, yet their relative strengths compared with supervised methods remain unclear. [...] Read more.
Clinical discharge summaries contain rich patient information but remain difficult to convert into structured representations for downstream analysis. Recent advances in large language models (LLMs) have introduced new approaches for clinical text extraction, yet their relative strengths compared with supervised methods remain unclear. This study presents a controlled evaluation of three dominant strategies for structured clinical information extraction from electronic health records: prompting-based extraction using LLMs, retrieval-augmented generation for terminology canonicalization, and supervised fine-tuning of domain-specific transformer models. Using discharge summaries from the MIMIC-IV dataset, we compare zero-shot, few-shot, and verification-based prompting across closed-source and open-source LLMs, evaluate retrieval-augmented canonicalization as a post-processing mechanism, and benchmark these methods against a fine-tuned BioClinicalBERT model. Performance is assessed using a multi-level evaluation framework that combines exact matching, fuzzy lexical matching, and semantic assessment via an LLM-based judge. The results reveal clear tradeoffs across approaches: prompting achieves strong semantic correctness with minimal supervision, retrieval augmentation improves terminology consistency without expanding extraction coverage, and supervised fine-tuning yields the highest overall accuracy when labeled data are available. Across all methods, we observe a consistent 40−50% gap between exact-match and semantic correctness, highlighting the limitations of string-based metrics for clinical Natural Language Processing (NLP). These findings provide practical guidance for selecting extraction strategies under varying resource constraints and emphasize the importance of evaluation methodologies that reflect clinical equivalence rather than surface-form similarity. Full article
(This article belongs to the Special Issue Advanced Algorithms for Biomedical Data Analysis)
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11 pages, 1220 KB  
Article
A Method for Calculating Whole-Genome Sequencing Outcomes from Trio Data
by Nikita Koltunov, Egor Guguchkin, Oleg Samovarov, Liudmila Mikhailova and Evgeny Karpulevich
Algorithms 2025, 18(10), 610; https://doi.org/10.3390/a18100610 - 29 Sep 2025
Viewed by 1480
Abstract
Background. Whole-genome sequencing (WGS) enables comprehensive detection of genetic variants but faces limitations in benchmarking due to incomplete reference datasets. Trio-based analysis, leveraging Mendelian inheritance, provides an alternative strategy for validating sequencing results and estimating error rates, particularly in regulatory genomic regions. [...] Read more.
Background. Whole-genome sequencing (WGS) enables comprehensive detection of genetic variants but faces limitations in benchmarking due to incomplete reference datasets. Trio-based analysis, leveraging Mendelian inheritance, provides an alternative strategy for validating sequencing results and estimating error rates, particularly in regulatory genomic regions. Methods. We extended the nf-core/sarek WGS pipeline by integrating a module that collects parental and offspring allele information, extracts regulatory genomic regions, and computes Mendelian-consistency scores. The algorithm processes variant calls from parents and children to identify expected versus anomalous inheritance patterns. The module was implemented in C++ and integrated into the Nextflow workflow, supporting automated analysis of trio datasets. Results. The method was validated on two real trio datasets, comparing DeepVariant and HaplotypeCaller as variant callers. For both trios, DeepVariant consistently achieved higher sensitivity and precision, with statistically significant differences confirmed using 95% confidence intervals. These results demonstrate that the proposed approach enables effective benchmarking of variant-calling performance in non-benchmark datasets. Conclusions. The developed method provides a practical and scalable framework for quantifying WGS outcomes from trio data. By incorporating Mendelian-inheritance validation into existing pipelines, researchers can estimate sequencing error rates, compare variant callers, and optimize workflows in regulatory genomic regions. Our findings confirm the superior performance of DeepVariant over HaplotypeCaller for the studied datasets. Full article
(This article belongs to the Special Issue Advanced Algorithms for Biomedical Data Analysis)
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24 pages, 2160 KB  
Article
Agentic RAG-Driven Multi-Omics Analysis for PI3K/AKT Pathway Deregulation in Precision Medicine
by Micheal Olaolu Arowolo, Sulaiman Olaniyi Abdulsalam, Rafiu Mope Isiaka, Kingsley Theophilus Igulu, Bukola Fatimah Balogun, Mihail Popescu and Dong Xu
Algorithms 2025, 18(9), 545; https://doi.org/10.3390/a18090545 - 30 Aug 2025
Cited by 3 | Viewed by 2138
Abstract
The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision [...] Read more.
The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article
(This article belongs to the Special Issue Advanced Algorithms for Biomedical Data Analysis)
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