Bei‐Wen Ying

The paper investigates bacterial growth and community changes in public restrooms under varying nutrient conditions and cleaning, finding nutrient availability drives community assembly more than cleaning.

This study shows that habitat reconstruction and genome mutation can equally increase bacterial fitness through experimental evolution and active learning.

This study uses active learning to optimize medium components for selective culture of Lactobacillus plantarum and Escherichia coli, improving growth specificity through machine learning models trained on growth curve data.

This study uses active learning with gradient-boosting decision trees to optimize mammalian cell culture medium, reducing fetal bovine serum and improving NAD(P)H levels.

The paper uses machine learning to optimize medium composition for improved production of foreign and native metabolites in Escherichia coli.

The paper uses machine learning to analyze bacterial growth data and identifies environmental factors influencing growth phases in Escherichia coli.

The paper identifies a common chromosomal periodicity in bacterial transcriptomes correlated with growth rate, providing a global parameter for evaluating bacterial adaptiveness.