Yixin Chen

This paper introduces CellTok, a method to convert single-cell transcriptomes into tokens for LLMs, enabling joint processing of cellular data and text.

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.

mosGraphGen generates multi-omics signaling graphs to enable integrative and interpretable graph AI model development by converting multi-omics data into biologically meaningful graphs.

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 introduces DeepSignalingFlow, a deep learning model that predicts drug combination synergy and interprets the mechanisms of synergy using graph neural networks.

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