Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article A Machine Learning Framework for Advanced Analytical Detection of CD36 Using Immunosensors Below Limit of Detection(Elsevier Ltd, 2026) Yeke, M.C.; Gelen, S.S.; Fil, H.; Yalcin, M.M.; Gumus, A.; Yazgan, I.; Odaci, D.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. © 2026 The Author(s)Article Citation - WoS: 3Citation - Scopus: 3Data Driven Modeling and Design of Cellulose Acetate-Polysulfone Blend Ultrafiltration Membranes Based on Artificial Neural Networks(Elsevier Ltd, 2025) Gungormus, E.This study aimed to develop and validate an Artificial Neural Networks (ANNs) model for the design and optimization of cellulose acetate-polysulfone blend ultrafiltration membranes, produced via the Non-Solvent Induced Phase Separation method. After some data science applications on a comprehensive dataset obtained from literature studies, the ultimate ANNs model exhibited superior predictive capabilities and effectively captured complex nonlinear relationships in the data. The optimum model configuration with a single hidden layer containing six neurons provided reliable predictions by avoiding overfitting and underfitting risks and significantly reducing error metrics. The model analyzed the effects of input variables on outputs, revealing that different stages of the membrane preparation process had varying impacts on performance metrics. This finding emphasized the importance of systematically optimizing the preparation process to enhance overall membrane performance. The model's predictions showed strong agreement with experimental data, further validating its accuracy. The optimum production conditions identified by the model offered significant improvements in membrane performance. Moreover, the model accelerated the membrane development process by reducing the required number of experimental trials and promoting efficient resource utilization. This approach contributed to both economic and environmental sustainability by reducing production costs and energy consumption. This study highlighted the significant potential of machine learning techniques for future innovations and advancements in this field by enabling precise, efficient, and sustainable membrane design and synthesis. © 2025 Elsevier Ltd.