Robert R. Butler

SPHERE enables the use of sensitive datasets in AI and open science by creating synthetic twins that preserve statistical properties and privacy.

This paper introduces a framework for identifying disease-associated features tailored to individual profiles using localized test statistics, applied to single-cell RNA sequencing data for Alzheimer's disease.

The paper identifies cross-domain drivers of Alzheimer's disease risk by integrating network analysis across biological domains.

BD10-2, a TrkB/C partial agonist, improves memory and LTP deficits in Alzheimer's mice by modulating synaptic signaling and microglial transcriptomic changes.

PhenotypeXpression identifies disease subtypes by clustering gene expression data and analyzing literature to create phenotypic profiles.