Liam D. Kirkpatrick

Machine learning guided design of strong transcriptional activation domains

The paper designs functional transcriptional activation domains using machine learning and experimental validation, finding that over 90% of predicted sequences work in yeast.

Causes and consequences of experimental variation in Nicotiana benthamiana transient expression

The paper models variability in Nicotiana benthamiana transient expression and provides guidelines for minimizing it through experimental design and power analysis.

Evolutionary discovery and characterization of fungal transcriptional activators using active learning

The paper develops ADhunter, a model to identify and quantify transcriptional activators in fungal genomes, expanding functional annotations across non-model species.

The pGinger family of expression plasmids

The paper introduces a family of 43 expression plasmids for precise gene expression in gram-negative bacteria, with constitutive and inducible systems tested in Escherichia coli and Pseudomonas putida.