Shannon K. McWeeney

This paper develops a rapid qPCR-based gene expression profiler to predict AML patient response to standard therapies using cell state and BCL2 family gene expression signatures.

The paper investigates compound interactions in Centella asiatica using multi-omics analysis on mouse neurons, revealing both positive and negative effects on gene and metabolite profiles.

The paper defines AI-readiness criteria for biomedical data, encompassing seven dimensions, and evaluates them across flagship datasets to ensure ethical and explainable AI practices.

This paper introduces Graph Structured Neural Networks (GSNN) for perturbation biology, showing they outperform baselines in predicting perturbed gene expression, drug combinations, and disease-specific drug prioritization.

The paper develops a framework to analyze scRNA-seq data and identify gene-regulatory networks linked to CD8 T cell exhaustion in tumor and viral infection contexts.

This study uses RNA base editing to repair a mutation in a mouse model of Rett syndrome, restoring MeCP2 function in hippocampal neurons.

This study evaluates two computational methods for removing contaminants in low biomass 16S rRNA gene sequencing, finding that Decontam consistently outperforms SourceTracker in contaminant removal.

The paper creates synthetic clinical cardiovascular data and a curriculum to teach data science fundamentals, finding mixed student performance in applying machine learning techniques.