Data Modeling and Algorithms in Biomedical Applications

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 865

Special Issue Editor


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Laboratory of Data Science and Modeling, Department of Statistics and Econometrics, Faculty of Economics and Business Administration, Sofia University "St. Kl. Ohridski", 125 Tsarigradsko Shosse Blvd., bl.3, 1113 Sofia, Bulgaria
Interests: modeling; machine learning; algorithms
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Special Issue Information

Dear Colleagues,

The field of bioengineering has witnessed significant advancements in the development and application of computer models and algorithms for various biomedical applications. The integration of computational methods and techniques has revolutionized the way in which approach complex problems in medicine and healthcare, offering a unique opportunity to enhance diagnosis, treatment, and patient care.

This Special Issue aims to explore the latest research and innovations in computer models and algorithms when applied to biomedical applications. The scope of this Special Issue includes, but is not limited to, the following topics: predictive modeling, data and image analysis, data mining, machine learning, AI applications, and simulation techniques for understanding biological processes, disease progression, and treatment outcomes. By leveraging computational tools and methodologies, researchers and practitioners can gain insights into the intricate mechanisms that underlie various physiological and pathological conditions, ultimately leading to more effective and personalized healthcare solutions.

We invite researchers, academics, and industry professionals to submit their original research articles, reviews, and perspectives to this Special Issue. By connecting experts from diverse backgrounds, we hope to foster collaboration and the exchange of ideas in order to advance the field of bioengineering.

Prof. Dr. Ivan Ganchev Ivanov
Guest Editor

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Keywords

  • computer models
  • algorithms
  • biomedical applications
  • predictive modeling
  • machine learning
  • data analysis
  • data modeling
  • AI applications

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Published Papers (1 paper)

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Research

21 pages, 3456 KiB  
Article
Precision in 3D: A Fast and Accurate Algorithm for Reproducible Motoneuron Structure and Protein Expression Analysis
by Morgan Highlander, Shelby Ward, Bradley LeHoty, Teresa Garrett and Sherif Elbasiouny
Bioengineering 2025, 12(7), 761; https://doi.org/10.3390/bioengineering12070761 - 14 Jul 2025
Viewed by 252
Abstract
Structural analysis of motoneuron somas and their associated proteins via immunohistochemistry (IHC) remains tedious and subjective, requiring costly software or adapted 2D manual methods that lack reproducibility and analytical rigor. Yet, neurodegenerative disease and aging research demands precise structural comparisons to elucidate mechanisms [...] Read more.
Structural analysis of motoneuron somas and their associated proteins via immunohistochemistry (IHC) remains tedious and subjective, requiring costly software or adapted 2D manual methods that lack reproducibility and analytical rigor. Yet, neurodegenerative disease and aging research demands precise structural comparisons to elucidate mechanisms driving neuronal degeneration. To address this need, we developed a novel algorithm that automates repetitive and subjective IHC analysis tasks, enabling thorough, objective, blinded, order-agnostic, and reproducible 3D batch analysis. With no manual tracing, the algorithm produces 3D Cartesian reconstructions of motoneuron somas from 60× IHC images of mouse lumbar spinal tissue. From these reconstructions, it measures 3D soma volume and efficiently quantitates net somatic protein expression and macro-cluster size. In this validation study, we applied the algorithm to assess soma size and C-bouton expression in various healthy control mice, comparing its measurements against manual measurements and across multiple algorithm users to confirm its accuracy and reproducibility. This novel, customizable tool enables efficient and high-fidelity 3D motoneuron analysis, replacing tedious, qualitative, cell-by-cell manual tuning with automatic threshold adaptation and quantified batch settings. For the first time, we attain reproducible results with quantifiable accuracy, exhaustive sampling, and a high degree of objectivity. Full article
(This article belongs to the Special Issue Data Modeling and Algorithms in Biomedical Applications)
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