Leslie M. J. Shaw

This study identifies IRS2 as a mediator of resistance to PI3K pathway inhibitors in PIK3CA mutant breast cancer by sustaining PI3K signaling through FOXO3-mediated regulation.

This study uses machine learning to show that integrating brain connectome data with amyloid-beta PET imaging improves prediction of cognitive decline in older adults.

The paper investigates whether hemispheric asymmetry in tau pathology in Alzheimer's disease is linked to reduced inter-hemispheric connectivity or asymmetric amyloid-beta distribution, finding a strong association with amyloid-beta laterality.

This paper evaluates the polygenic risk score for Alzheimer's disease in Japanese individuals, showing its utility despite genetic differences from European populations.

The paper develops an APOE genetic risk score based on neuropathology that better explains CSF biomarker variance than traditional APOE ε4 metrics.

This study uses an epidemic spreading model to show that tau spreads through neuronal connections in Alzheimer's disease, with beta-amyloid accelerating this process in some brain regions.

This paper evaluates the ATN framework's accuracy in diagnosing Alzheimer's disease subtypes and frontotemporal lobar degeneration using cerebrospinal fluid biomarkers, finding that non-amnestic Alzheimer's patients have lower tau levels and the framework is less sensitive to frontotemporal lobar degeneration.

The paper investigates sex and APOE ε4 genotype effects on metabolic alterations in Alzheimer’s disease using stratified analysis of serum metabolites.

This study compares whole hippocampus and subfield biomarkers for Alzheimer’s disease detection and prediction using multimodal MRI data, finding subiculum biomarkers improve prediction accuracy.

The paper investigates the association between triglyceride principal component scores and Alzheimer's disease biomarkers in the ADNI cohort, finding significant links with MCI, AD, and specific brain structures.

This paper develops a blood-based machine learning model to predict cerebral spinal fluid Aβ1-42 levels, showing high accuracy in identifying Alzheimer's disease risk.

The paper identifies altered bile acid profiles in Alzheimer's disease, linking them to cognitive impairment and genetic variants through a large study.

The paper identifies associations between serum bile acid metabolites and AD biomarkers in the A/T/N framework, suggesting a potential role for bile acid pathways in AD pathophysiology.

This paper introduces SuStaIn, a machine learning method that identifies disease subtypes and their temporal progression from cross-sectional data, improving prediction of diagnostic conversion in neurodegenerative diseases.

The paper examines the heritability of regional brain volumes using genetic and neuroimaging data from multiple cohorts, finding high heritability and consistent patterns across populations.