Jiarui Feng

PathFinder infers multi-cell signaling pathways and communications using a graph transformer model, identifying novel interactions in AD mouse models.

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

This study uses deep learning models and single-cell RNA-seq data to identify molecular targets and signaling pathways involved in macrophage-fibroblast-cardiomyocyte interactions in heart failure.

This study introduces a novel interpretable k-hop graph attention network to integrate multi-omics data and infer essential targets and signaling networks, showing improved performance over existing models.

The paper develops a deep learning model to analyze the relevance of cancer signaling pathways to patient survival across four cancer types.

The paper introduces a deep learning model to predict signaling cascades by integrating PPI data and omics data, validated using curated KEGG pathways.

This study introduces DeepSignalingSynergy, a simplified deep learning model that predicts tumor cell response to drug combinations using cancer signaling pathways, showing feasibility with NCI ALMANAC data.