Modeling Metabolic Dynamics
By leveraging time derivatives of metabolic pathways, insights are gained into system characteristics through traditional machine learning models like random forests. The approach utilizes a meta learner to identify optimal models, which then enable the generation of new time series by simulating trajectories, akin to a ball rolling down a hill. This innovative method expands the dataset and enhances understanding of metabolic states over time.In this clip
From this podcast
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
Predicting Metabolic Pathway Dynamics with Machine Learning, w/ Zak Costello - #163
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