A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors

Khodadadian, Ehsan; Mirsian, Samaneh; Shashaani, Shahrzad; Parvizi, Maryam; Khodadadian, Amirreza; Knees, Peter; Hilber, Wolfgang; Heitzinger, Clemens

Abstract

Graphene Field-Effect Transistors (GFETs) are gaining prominence in enzyme detection due to their exceptional sensitivity, rapid response, and capability for real-time monitoring of enzymatic reactions. Among different catalytic systems, heme-based peroxidase enzymes such as horseradish peroxidase (HRP), and heme molecules, which can exhibit peroxidase-like activity, are noteworthy due to their significant catalytic behavior. GFETs effectively monitor and detect these enzymatic reactions by observing alterations in their electrical properties. In this study, we present a computational framework designed to determine key enzymatic parameters, including the enzyme turnover number and the Michaelis–Menten constant. Utilizing experimental reaction rate data obtained from the GFET electrical response, we apply Bayesian inversion models to estimate these parameters accurately. Additionally, we develop a novel deep neural network (multilayer perceptron) to predict enzyme behavior under various chemical and environmental conditions. The performance of this coupled computational model is compared against standard machine learning and Bayesian inversion techniques to validate its efficiency and accuracy. We present a pseudocode to explain the implementation of machine learning Bayesian inversion framework.

Citation

Khodadadian, E., Mirsian, S., Shashaani, S., Parvizi, M., Khodadadian, A., Knees, P., Hilber, W., & Heitzinger, C. (2025). A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors. Machine Learning with Applications, 21, Article 100718. https://doi.org/10.1016/j.mlwa.2025.100718

• Journal Article Type: Article
• Acceptance Date: Jul 26, 2025
• Online Publication Date: Aug 12, 2025
• Publication Date: 2025-09
• Deposit Date: Aug 18, 2025
• Journal: Machine Learning with Applications
• Electronic ISSN: 2666-8270
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 21
• Article Number: 100718
• DOI: https://doi.org/10.1016/j.mlwa.2025.100718
• Public URL: https://keele-repository.worktribe.com/output/1367428
• Publisher URL: https://www.sciencedirect.com/science/article/pii/S266682702500101X?via%3Dihub