Ryan Courtney Fields

BioMedGraphica integrates biomedical knowledge into a unified graph and introduces a Textual-Numeric Graph structure for multi-omics analysis.

The paper introduces CytoTRACE 2, a deep learning framework to assess cell potency from scRNA-seq data, showing improved performance across multiple datasets and applications.

This study identifies TAK1 as a therapeutic target in pancreatic cancer that enhances anti-tumor immunity and sensitizes tumors to checkpoint blockade.

OmniCellAgent autonomously retrieves and analyzes scRNA-seq datasets to generate structured reports and hypotheses across disease settings.

M3NetFlow is a multi-scale multi-hop graph model for multi-omic data analysis that ranks targets and infers signaling pathways, achieving high accuracy in drug response and Alzheimer's disease studies.

SC2MeNetDrug is a tool that identifies cell-cell signaling targets and predicts drugs using scRNA-seq data to analyze disease microenvironments.

The paper identifies functional non-coding variants in melanoma, showing that CDC20 promoter variants reduce transcriptional activity and alter melanoma phenotypes.

The paper presents a pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis of tumor regions and xenograft-transplant lymphoproliferative disorder prediction.