Gregory F. Cooper

Causal Modeling Reveals Cell-Cell Communication Dynamics in the Tumor Microenvironment During Anti-PD-1 Therapy in Breast Cancer Patients

This study uses single-cell RNA-seq data to analyze cell-cell communication changes in breast cancer patients during anti-PD-1 therapy, identifying key pathways and potential therapeutic targets.

Inferring Personalized Cell-Cell Communication Networks in Colorectal Cancer with Individualized Causal Discovery

This paper develops a framework to infer individualized cell-cell communication networks in colorectal cancer using causal discovery methods applied to single-cell RNA-seq data.

Deconvoluting single-cell transcriptomics reveals cellular programs regulated by cell-cell communication in colorectal cancer

The paper uses single-cell data to identify gene expression modules regulated by cell-cell communication in colorectal cancer and validates findings with spatial transcriptomics.

An interpretable deep learning framework for genome-informed precision oncology

This paper introduces a deep learning framework that improves drug response prediction in cancer by modeling cellular state from somatic genome alterations.

Tumor-specific Causal Inference (TCI): A Bayesian Method for Identifying Causative Genome Alterations within Individual Tumors

This paper introduces a Bayesian method to identify causative genome alterations in individual tumors by inferring their impact on transcriptomic changes.