Ken Wang

This paper compares in silico predictions of action potential duration changes with new human ex vivo recordings under IKr and ICaL inhibition, finding that no model accurately predicts all experimental outcomes.

This paper introduces filter inference, a scalable method for estimating NLME model parameters from snapshot time series data, enabling efficient analysis of large datasets.

The paper shows that model selection based on AIC or model averaging can lead to unreliable treatment response predictions due to nonlinear dynamics, and proposes probabilistic averaging as a more reliable approach.