This special session addresses multimodal data challenges in drug repositioning and disease biomarker discovery, focusing on AI-powered innovations using biomedical signals (e.g., EEG/ECG) and medical images (e.g., MRI/CT). Topics include deep learning, multi-omics fusion, interpretable AI, and dynamic network analysis, bridging computational models with clinical translation. We invite interdisciplinary submissions to advance intelligent drug development and disease diagnostics.

Track Committee
• Track Chair
Assoc. Prof. Hailin Chen, East China Jiaotong University

Topics
Novel deep learning methods for drug-disease association prediction
Medical image feature extraction for biomarker discovery
Multi-omics data fusion strategies for drug repositioning
Disease subtyping biomarkers via bioelectric signal analysis (EEG/ECG)
Integrative modeling of histopathology images and genomics for target identification
Federated learning for cross-institutional drug repositioning
Dynamic biological network analysis for drug mechanism interpretation
Fusion of radiomics features and liquid biopsy data
Single-cell transcriptomics-guided computational drug repurposing
Interpretable AI for biomarker visualization
Signal processing algorithms for non-coding RNA biomarker identification
Adverse drug reaction signal mining for repositioning risk assessment
3D medical image reconstruction for tumor microenvironment studies
Metabolomics signal processing for early disease diagnosis
Graph neural networks for drug-target interaction prediction
Real-time biomarker detection using ultrasound/optical imaging
EHR and biosignal data integration for repurposing hypotheses
Microbiome signal analysis for chronic disease drug development
Automated histopathology analysis for biomarker validation
Blockchain-enabled distributed biomarker data sharing

Publication