ArticleBMC bioinformatics2022
MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- How far can you go? Extrapolating values of catalytic activity from known protein landscapes in natural and directed evolution.Chemical Society reviews · 2026Review
- Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026Review
- IECata: interpretable bilinear attention network and evidential deep learning improve the catalytic efficiency prediction of enzymes.Briefings in bioinformatics · 2025Article
- DEKP: a deep learning model for enzyme kinetic parameter prediction based on pretrained models and graph neural networks.Briefings in bioinformatics · 2025Article
- GraphKM: machine and deep learning for KBMC bioinformatics · 2024Article
- Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT.International journal of molecular sciences · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundKinetic modeling is a powerful tool for understanding the dynamic behavior of biochemical systems. For kinetic modeling, determination of a number of kinetic parameters, such as the Michaelis constant (K
resultsTo solve these problems, we propose the Machine Learning-Aided Global Optimization (MLAGO) method for K
conclusionsMLAGO overcomes the major problems in parameter estimation, accelerates kinetic modeling, and thus ultimately leads to better understanding of complex cellular systems. The web application for our machine learning-based K
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Registered trials
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