A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors
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 (...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-09-01
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| Series: | Machine Learning with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266682702500101X |
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| author | Ehsan Khodadadian Samaneh Mirsian Shahrzad Shashaani Maryam Parvizi Amirreza Khodadadian Peter Knees Wolfgang Hilber Clemens Heitzinger |
| author_facet | Ehsan Khodadadian Samaneh Mirsian Shahrzad Shashaani Maryam Parvizi Amirreza Khodadadian Peter Knees Wolfgang Hilber Clemens Heitzinger |
| author_sort | Ehsan Khodadadian |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-dddf97869be94ffc9b48518a3a8ffe03 |
| institution | DOAJ |
| issn | 2666-8270 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Machine Learning with Applications |
| spelling | doaj-art-dddf97869be94ffc9b48518a3a8ffe032025-08-20T03:04:54ZengElsevierMachine Learning with Applications2666-82702025-09-012110071810.1016/j.mlwa.2025.100718A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistorsEhsan Khodadadian0Samaneh Mirsian1Shahrzad Shashaani2Maryam Parvizi3Amirreza Khodadadian4Peter Knees5Wolfgang Hilber6Clemens Heitzinger7Machine Learning Unit, TU Wien, Vienna, AustriaInstitute of Microelectronics and Microsensors, Johannes Kepler University Linz, AustriaFaculty of Informatics, TU Wien, Vienna, AustriaMachine Learning Unit, TU Wien, Vienna, Austria; School of Mathematics, University of Birmingham, Birmingham, United KingdomSchool of Computer Science and Mathematics, Keele University, Staffordshire, United Kingdom; Machine Learning Unit, TU Wien, Vienna, Austria; Corresponding author at: School of Computer Science and Mathematics, Keele University, Staffordshire, United Kingdom.Faculty of Informatics, TU Wien, Vienna, AustriaInstitute of Microelectronics and Microsensors, Johannes Kepler University Linz, AustriaMachine Learning Unit, TU Wien, Vienna, AustriaGraphene 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.http://www.sciencedirect.com/science/article/pii/S266682702500101XMichaelis–Menten parametersEnzymeGraphene field-effect transistorsBayesian inversionDeep neural networks |
| spellingShingle | Ehsan Khodadadian Samaneh Mirsian Shahrzad Shashaani Maryam Parvizi Amirreza Khodadadian Peter Knees Wolfgang Hilber Clemens Heitzinger A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors Machine Learning with Applications Michaelis–Menten parameters Enzyme Graphene field-effect transistors Bayesian inversion Deep neural networks |
| title | A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors |
| title_full | A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors |
| title_fullStr | A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors |
| title_full_unstemmed | A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors |
| title_short | A Bayesian inversion supervised learning framework for the enzyme activity in graphene field-effect transistors |
| title_sort | bayesian inversion supervised learning framework for the enzyme activity in graphene field effect transistors |
| topic | Michaelis–Menten parameters Enzyme Graphene field-effect transistors Bayesian inversion Deep neural networks |
| url | http://www.sciencedirect.com/science/article/pii/S266682702500101X |
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