(Include but not limited to)
Track 1: Neural Networks
Deep neural network architectures and structure design
Graph neural networks and graph representation learning
Convolutional neural networks and generative adversarial networks
Recurrent neural networks and sequence data modeling
Spiking neural networks and brain-inspired neuromorphic computing
Autoencoders and deep generative models
Self-supervised learning, contrastive learning, and meta-learning
Reinforcement learning and deep reinforcement learning
Transfer learning and multi-task learning
Neural-symbolic hybrid models and cognitive computing
Methods for interpretability and visualization of neural networks
Neural architecture search and automated machine learning
Model compression, knowledge distillation, and lightweight deployment
Federated learning and distributed model training
Applications of neural networks in bioinformatics and medical image analysis
Track 2: Bioinformatics and Systems Biology
Genomics, transcriptomics, and proteomics analysis
Integration of multi-omics data and graph machine learning
Gene regulatory networks, metabolic networks, and signaling pathway inference
Disease network modeling and disease module identification
Systems biology modeling and multi-scale biological simulation
Single-cell data analysis and spatial transcriptomic intelligent computing
Disease gene prioritization and drug repositioning
Evolutionary genetics and computational evolutionary biology
Structural bioinformatics and protein structure prediction
Discovery of biomarkers and precision medicine
Cross-species knowledge transfer and model organism research
Causal discovery and disease progression reasoning
DNA computing and biomolecular computing
Synthetic biology and biological system design
Biostatistics and stochastic modeling
Track 3: Intelligent Computing
Applications of artificial intelligence in biomedical signal processing
Large language models in biomedical knowledge extraction and systems biology
Prompt engineering and LLM fine-tuning for life science research
Knowledge graph construction and biomedical data fusion
Digital twin-driven biological system analysis and experimental design
Interpretable artificial intelligence and trustworthy AI in disease network biology
Computer vision in biomedical image analysis
Brain-computer interface and neural signal decoding technology
Biological data mining, pattern recognition, and feature selection
Automated experimental systems and intelligent laboratory-driven
High-performance computing and cloud computing in bioinformatics
Multimodal data fusion and cross-modal bioinformatics analysis
Data science, big data analysis, and integrated mining
Bioinformatics algorithms, software tools, and benchmark evaluation framework
Bio-network modeling and computational molecular systems ......