Rebecca L. Johnston

The paper uses computational drug repurposing to identify N-acetylglucosamine as a potential therapy for CLN3 Batten disease, showing it reduces lipofuscin burden in cell models.

The paper develops machine learning models to predict pathogenicity of BRCA1 and BRCA2 variants using cancer genomic profiles, improving classification of variants of uncertain significance.

This study identifies prenylation as a regulator of cardiomyocyte proliferation through multiple targets, suggesting its potential for cardiac regeneration.

The paper identifies sequential transcription factor programs that govern the activation of quiescent hippocampal stem cells through single-cell transcriptomic analysis in mice.

This study shows that APOE4 increases tau levels in brain endothelial cells, reducing anti-tau antibody delivery across the blood-brain barrier in vitro.

This paper benchmarks Bayesian deep learning models for predicting cancer of unknown primary, showing improved uncertainty generalisation and accuracy under distributional shifts.

This study demonstrates that focused ultrasound with microbubbles enhances the delivery of Aducanumab and anti-Tau antibodies across a human sporadic Alzheimer's disease blood-brain barrier model.

This paper uses CRISPR/Cas9 to edit the acetylcholinesterase gene in Schistosoma mansoni eggs, resulting in reduced AChE activity and decreased granuloma size in mice.