Spatial Transcriptomics & Drug Sensitivity Research

Multi-modal spatial transcriptomics and histology image registration for drug response prediction

Project Overview

Led cutting-edge research in spatial transcriptomics and drug sensitivity analysis at the Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, focusing on multi-modal data integration and AI-driven drug response prediction.

Research Projects

Spatial Transcriptomics-Histology Image Registration (2025.03-2025.06)

Role: Core Research Member at GIBH, CAS

Technical Contributions

  • End-to-End Registration Framework: Developed comprehensive alignment system for spatial transcriptomics data and histology images
  • Hybrid Graph Neural Networks: Designed advanced GNN architecture for processing large-scale spatial point data
  • Multi-scale Feature Extraction: Focused on mouse brain tissue spatial transcriptomics analysis and spatial feature extraction
  • Data Engineering: Led data preprocessing, quality control, and feature engineering pipelines

Key Achievements

  • Precision Alignment: Achieved high-precision spatial data alignment between transcriptomics and histology modalities
  • Scalable Processing: Successfully processed large-scale spatial datasets with improved computational efficiency
  • Biological Insights: Generated novel insights into spatial gene expression patterns in brain tissue

STADIUM: Spatial Transcriptomics-Driven Intelligent AI for Understanding Medicine (2025.06-2025.08)

Role: Core Research Member at GIBH, CAS

Project Vision

Addressing the critical challenges in precision drug response prediction for personalized cancer treatment, focusing on lung cancer spatial heterogeneity and multi-modal data integration.

Technical Innovation

  • Multi-modal Integration: Integrated spatial transcriptomics, histopathology, and pharmacogenomics data
  • Heterogeneous Knowledge Graph: Constructed drug-target-pathway-microenvironment-morphology knowledge network
  • Multi-modal Knowledge Fusion Network (MKFN): Developed advanced fusion architecture for spatial-molecular-morphological data
  • Interpretability Mechanisms: Implemented spatial attention, feature attribution, and counterfactual reasoning

Research Impact

  • Clinical Translation: Developed visualization decision support system for clinical applications
  • Precision Medicine: Advanced multi-modal AI for lung cancer precision treatment
  • Biological Understanding: Enhanced understanding of drug response mechanisms through spatial analysis

Technical Architecture

Data Processing Pipeline

  • Spatial Data Integration: Multi-modal data alignment and quality control
  • Feature Engineering: Advanced feature extraction from spatial and molecular data
  • Knowledge Graph Construction: Heterogeneous graph building for drug-target relationships

AI/ML Framework

  • Graph Neural Networks: Hybrid GNN architecture for spatial data processing
  • Multi-modal Fusion: Advanced fusion techniques for different data modalities
  • Interpretability: Multi-level explainability mechanisms for clinical trust

Key Contributions

Technical Leadership

  • Algorithm Development: Led core algorithm design and experimental validation
  • Data Engineering: Managed large-scale spatial transcriptomics datasets
  • Model Architecture: Designed innovative multi-modal fusion frameworks

Research Output

  • Clinical Applications: Developed practical tools for precision medicine
  • Biological Insights: Generated novel understanding of spatial gene expression
  • AI Innovation: Advanced multi-modal AI techniques for medical applications

This research should have represented a significant advancement in spatial transcriptomics and AI-driven drug discovery, and should have bridged computational biology with clinical applications, but due to certain reasons, it remains incomplete.

References