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

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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Bibliographic Details
Main Authors: Ehsan Khodadadian, Samaneh Mirsian, Shahrzad Shashaani, Maryam Parvizi, Amirreza Khodadadian, Peter Knees, Wolfgang Hilber, Clemens Heitzinger
Format: Article
Language:English
Published: Elsevier 2025-09-01
Series:Machine Learning with Applications
Subjects:
Michaelis–Menten parameters
Enzyme
Graphene field-effect transistors
Bayesian inversion
Deep neural networks
Online Access:http://www.sciencedirect.com/science/article/pii/S266682702500101X
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