Mehmet Umut Caglar

This study uses machine learning to predict bacterial growth conditions from mRNA and protein abundances in E. coli, showing that combined transcriptome and proteome data improve accuracy.

This study presents a comprehensive transcriptomics and proteomics dataset of E. coli under 34 growth conditions, revealing differences between exponential and stationary phases and differential gene expression under specific stresses.