Stephen Checkley

Engineering Tuneable Gene Circuits in Yeast

Stephen Checkley, 2015 .

Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738

P. Jasper, J. Tolsma, Stephen Checkley, 2015, Scientific Reports.

Demonstration of the Feasibility of Predicting the Flow of Pharmaceutically Relevant Powders from Particle and Bulk Physical Properties

H. Barjat, Stephen Checkley, N. Dawson, 2020, Journal of Pharmaceutical Innovation.

Using human in vitro transcriptome analysis to build trustworthy machine learning models for prediction of animal drug toxicity

Ritesh Krishna, Anna Paola Carrieri, Edward O. Pyzer-Knapp, 2020, Scientific Reports.

Combining human cell line transcriptome analysis and Bayesian inference to build trustworthy machine learning models for prediction of animal toxicity in drug development

Edward O. Pyzer-Knapp, A. Carrieri, Stephen Checkley, 2019, 1911.04374.

Corrigendum: Bridging the gap between in vitro and in vivo: Dose and schedule predictions for the ATR inhibitor AZD6738

Claus Bendtsen, Stephen Checkley, Linda MacCallum, 2016, Scientific Reports.

Demonstration of the Feasibility of Predicting the Flow of Pharmaceutically Relevant Powders from Particle and Bulk Physical Properties

H. Barjat, Stephen Checkley, N. Dawson, 2020, Journal of Pharmaceutical Innovation.