GET (Gene Expression Transformer) Implementation Mindmap

Implementation Status Overview

• Basic Setup Complete (Repository & Structure)
• Data Pipeline Ready
• Model Running
• Results Validation

Data Pipeline

• Input Processing
• Gene expression data preprocessing
• Normalization techniques
• Feature selection
• Batch effect correction

Model Architecture

• Transformer-based
• Self-attention layers (code available)
• Feed-forward networks (code available)
• Layer normalization (code available)
• Gene Expression Specific
• Gene-gene interaction modeling (code available)
• Pathway integration (code available)
• Expression pattern recognition (code available)

Training Pipeline

• Data splitting
• Training set preparation
• Validation set preparation
• Test set preparation
• Loss functions
• Expression prediction loss (code available)
• Regulatory network loss (code available)
• Optimization
• Learning rate scheduling (code available)
• Gradient clipping (code available)
• Early stopping (code available)

Inference Pipeline

• Batch processing
• Expression prediction
• Feature importance analysis
• Uncertainty quantification

Data Requirements

• Gene Expression Data
• PBMC data processed
• Peak sorting verified (chr1,chr2,chr3 order)
• Count matrix prepared
• Depth check (>3M recommended)
• Reference Data
• Gene annotation data
• Pathway databases
• Regulatory network information

Output Processing

• Analysis Tools
• Expression profile analysis
• Differential expression
• Pathway enrichment
• Visualization
• Expression heatmaps
• Network visualization
• PCA/t-SNE plots

Model Evaluation

• Performance Metrics
• MSE/MAE for expression prediction
• ROC/PR curves for classification
• Cross-validation
• Benchmarking against baselines