A Yeast9 GEM plus FBA, Random Forest, XGBoost, VAE, SHAP, Bayesian optimization and GAN framework yields a 12-fold biomass flux increase in simulated S. cerevisiae for SCP production.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
An Integrative Genome-Scale Metabolic Modeling and Machine Learning Framework for Predicting and Optimizing Single-Cell Protein Production in Saccharomyces cerevisiae
A Yeast9 GEM plus FBA, Random Forest, XGBoost, VAE, SHAP, Bayesian optimization and GAN framework yields a 12-fold biomass flux increase in simulated S. cerevisiae for SCP production.