Non-linearity of metabolic pathways critically influences the choice of machine learning model

HIGHLIGHTS

• who: Freu0301deu0301ric Cadet from the University of Paris, BIGR-Biologie Intu00e9gru00e9e du Globule Rouge, Inserm, UMR_S1134, Paris, France, Laboratory of have published the research: Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model, in the Journal: (JOURNAL)
• what: The authors show that random forest models are the most effective, with a high predictive capacity starting from predicted and experimental enzyme activities or experimental parameters collected from a bioreactor. The authors propose a means of decision support for researchers who wish to use machine_learning techniques as a starting or a complementary method . . .
  

   

  

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