GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes

Michaelis constant (K M ) is one of essential parameters for enzymes kinetics in the fields of protein engineering, enzyme engineering, and synthetic biology. As overwhelming experimental measurements of K M are difficult and time-consuming, prediction of the K M values from machine and deep learnin...

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Vydané v:	BMC bioinformatics Ročník 25; číslo 1; s. 1 - 12
Hlavní autori:	He, Xiao, Yan, Ming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 28.03.2024 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Algorithms Amino acids Application programming interface Artificial intelligence Bioinformatics Biology Biomedical and Life Sciences Computational Biology/Bioinformatics Computer Appl. in Life Sciences Datasets Deep learning Enzyme kinetics Enzymes Genetic aspects Graph neural network Graph neural networks Graphical representations Identification and classification Kinetics Learning algorithms Life Sciences Machine learning Michaelis constant Microarrays Mutants Mutation (Biology) Neural networks Prediction models Protein engineering Protein research Proteins Substrates Tree boosting China Deep learning Neural networks Michaelis constant Tree boosting Graph neural network
ISSN:	1471-2105, 1471-2105
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