Alan D. Stern

Network Inference from Perturbation Time Course Data

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.

Predicting Individual Cell Division Events from Single-Cell ERK and Akt Dynamics

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.

Gene-Specific Predictability of Protein Levels from mRNA Data in Humans

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

An Integrated Mechanistic Model of Pan-Cancer Driver Pathways Predicts Stochastic Proliferation and Death

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