Paul Geeleher

This paper develops an AI framework to interpret single-cell RNA-seq data from pediatric solid tumors, identifying tumor-specific surface antigen pairs for targeted therapies.

The paper develops ALTitude, a machine learning model to predict ALT status from whole genome sequencing data, identifies SMARCAL1 as a therapeutic target in ALT-driven cancers.

The paper investigates RA's limited effect on primary neuroblastoma tumors but efficacy in clearing bone marrow cells, identifying BMP signaling as a key determinant of cell fate and RA sensitivity.

This paper establishes a diverse collection of preclinical models for pediatric solid tumors and demonstrates their utility in precision medicine trials and drug testing.

This paper creates a spatial cell atlas of neuroblastoma, revealing tumor heterogeneity through multi-omics data and identifying spatial compartmentalization and signaling patterns.

This paper introduces a unified framework for gene set analysis that improves performance and interpretability by comparing effect size distributions between gene sets and subgroups.

The paper identifies lncRNAs as significant predictors of anticancer drug sensitivity, showing they can enhance drug response predictions beyond protein-coding genes.

The paper identifies cancer-specific eQTLs by modeling genotype-tumor purity interactions in heterogeneous tumor gene expression data, showing most risk variants affect tumor microenvironment cells.