Alan D. Stern

This study uses machine learning to predict cell division from ERK and Akt activity dynamics in single cells, finding ERK dynamics more predictive than Akt.

This paper introduces DL-MRA, a method to infer network structures from perturbation time course data, showing it can robustly identify small networks but has limitations for signaling networks.

The paper uses single-cell imaging and machine learning to predict cell division from ERK and Akt activity dynamics in MCF10A cells, finding ERK alone predicts division with AUC=0.74.

This study investigates factors affecting the reproducibility of in vitro drug-response assays across multiple centers, identifying context-sensitive factors that impact results.

The paper quantifies gene-specific predictability of protein levels from mRNA data across human tissues and identifies factors influencing this predictability.

The paper constructs a mechanistic model of pan-cancer driver pathways to predict stochastic proliferation and death in cancer cells.