1、 Core Machine Learning Algorithms and Computational Fundamentals
Innovation of Deep Learning, Graph Neural Networks, and Transformer Models for Biomedical Data
Representation Learning and Embedding Techniques for Biological Sequence and Structural Data
Machine learning methods for small sample, imbalanced, high-dimensional biomedical data
Application and Evaluation of Explainable Artificial Intelligence (XAI) in Biomedical Models
Application of Federated Learning, Privacy Computing, and Secure Multi party Computing in Health Data Collaboration
2、 Integration of genomics, proteomics, and multi omics
AI methods for genomic variation detection, functional annotation, and pathogenicity prediction
Integration analysis of single-cell multi omics data and construction of cell atlas
Gene regulatory network, pathway analysis, and systems biology modeling
Protein structure prediction, functional prediction, and protein-protein interactions
Computational modeling of spatial transcriptomics and spatiotemporal dynamic biological processes
3、 Drug Discovery and Chemical Biology
Deep learning methods for drug target discovery and virtual screening
AI model for molecular generation, optimization, and inverse synthesis planning
Drug target interaction prediction and pharmacophore modeling
Drug repositioning and multi pharmacological analysis
AI mining of biomarkers for patient stratification and efficacy prediction in clinical trials
4、 Medical Imaging and Pathological Informatics
Automatic segmentation, classification, and detection of radiological and pathological images
Fusion analysis and diagnosis of multimodal medical imaging (such as PET-MRI)
Quantitative analysis and prognosis prediction of digital pathology whole slice images
Automatic generation and enhancement of medical imaging reports
Disease progression modeling based on longitudinal imaging data
5、 Clinical Informatics and Health Data Science
Mining of Electronic Health Records (EHR): Phenotype Extraction, Risk Prediction, and Clinical Decision Support
AI pipeline generated from real-world data (RWD) and real-world evidence (RWE)
Intelligent analysis of wearable devices and continuous monitoring data
Disease subtype classification, complication prediction, and precision treatment recommendation system
Application of Natural Language Processing (NLP) in Clinical Text and Medical Literature Mining
6、 Conversion, Ethics, and System Applications
From Algorithms to Clinical Practice: Validation, Deployment, and Regulatory Science of AI Models
Fairness, Bias Alleviation, and Ethical Framework in Health Artificial Intelligence
Construction and Reasoning Application of Large scale Biomedical Knowledge Graph
Infectious Disease Transmission Modeling and Public Health AI
Computational models for aging research, longevity science, and health management