A machine learning framework for advanced analytical detection of CD36 using immunosensors below limit of detection
Yazarlar (7)
Muhammet Cagri Yeke
Izmir Yüksek Teknoloji Enstitüsü, Türkiye
Izmir Yüksek Teknoloji Enstitüsü, Türkiye
Sultan Sacide Gelen
Ege Üniversitesi, Türkiye
Ege Üniversitesi, Türkiye
Hilal Fil
Ege Üniversitesi, Türkiye
Ege Üniversitesi, Türkiye
Muhammet Mustafa Yalcin
Ege Üniversitesi, Türkiye
Ege Üniversitesi, Türkiye
Doç. Dr. Abdurrahman Gumus Isparta University Of Applied Sciences, Türkiye
Prof. Dr. İdris YAZGAN Kastamonu Üniversitesi, Türkiye
Prof. Dr. Dilek Odaci Ege Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SCOPUS dergilerinde yayınlanan tam makale)
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| Dergi Adı | Biosensors and Bioelectronics X | ||
| Dergi ISSN | 2590-1370 Dergi Bilgileri (2026) | ||
| Makale Dili | İngilizce | Basım Tarihi | 03-2026 |
| Cilt / Sayı / Sayfa | 28 / 1 / – | DOI | 10.1016/j.biosx.2025.100733 |
| Makale Linki | https://doi.org/10.1016/j.biosx.2025.100733 | ||
| UAK Araştırma Alanları |
Fen Bilimleri ve Matematik
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| Özet |
| We introduce a machine learning (ML)-based regression framework for quantitative electrochemical analysis, representing a paradigm shift from traditional univariate methods to a multivariate approach. Conventional analysis is constrained by reducing the entire signal to a single peak current feature to define a linear range and calculate a limit of detection (LOD). In contrast, our methodology treats the Differential Pulse Voltammetry (DPV) curve as time-series data, creating a high-dimensional fingerprint by systematically evaluating multiple data windows with varying widths around the main signal peak to identify the most informative segment. To validate this approach, a biosensor was developed by immobilizing Anti-CD36 antibodies on polydopamine-modified screen-printed carbon electrodes for the detection of CD36, a key protein in metabolism and immunity. Measurements were collected across 12 concentrations, including blank samples, spanning a range of 0 to 25 ng/mL. Following data augmentation, nine different regression models were evaluated, with the top-performing models achieving near-perfect prediction accuracy (R2>0.99) across this entire range. This high accuracy across the full concentration spectrum quantitatively demonstrates the method's ability to operate without relying on traditional concepts like linear range or LOD, enabling reliable detection at ultra-low levels. Furthermore, the immunosensor exhibited high selectivity against common interferents and excellent recovery in human serum. This methodology represents a significant advancement in analytical electrochemistry, providing a transferable approach for enhancing sensitivity in biomarker detection with potential applications in clinical diagnostics and biomedical research. The codes and dataset are made publicly available on GitHub to support further research: https://github.com/miralab-ai/biosensors-AI. |
| Anahtar Kelimeler |
| Augmentation | Biosensor | CD36 | Machine learning | Regression |
Science Direct
A machine learning framework for advanced analytical detection of CD36 using immunosensors below limit of detection
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Scopus | 2 |