# Hierarchical Meta-Learning for Cancer Pathway Signatures - Manuscript Summary

## Overview
This document summarizes the complete NeurIPS 2025 manuscript created for the Agents4Science track.

## Files Created
- **Main manuscript**: `agent4science/manuscript/hierarchical_meta_learning_neurips2025.tex`
- **Style file**: `agent4science/manuscript/agents4science_2025.sty`
- **PDF output**: `agent4science/manuscript/hierarchical_meta_learning_neurips2025.pdf`

## Document Structure (13 pages)

### Core Content (8 pages):
1. **Abstract** (250 words) - Highlights technical novelty and biological impact
2. **Introduction** - Positions work in ML and computational biology contexts
3. **Related Work** - Meta-learning, cancer genomics, hierarchical learning
4. **Method** - Hierarchical MAML with pathway attention mechanisms
5. **Experiments** - TCGA dataset, baselines, evaluation metrics
6. **Results** - Few-shot performance, pathway analysis, transferability
7. **Discussion** - Technical contributions, biological insights, clinical implications
8. **Conclusion** - Key contributions and broader impact

### Appendices (3 pages):
- Technical specifications and hyperparameter sensitivity
- Additional baseline comparisons
- Agents4Science AI Involvement Checklist
- Agents4Science Paper Checklist

## Key Technical Features

### Novel Contributions:
- **Hierarchical MAML Architecture**: 3-level hierarchy (organ → histology → molecular)
- **Pathway-Aware Attention**: Focus on biologically relevant gene sets
- **Multi-Level Loss Function**: Balances predictions across hierarchical levels
- **Cross-Cancer Transferability Analysis**: Quantifies pathway conservation

### Experimental Results:
- **Dataset**: 12,226 samples, 36 cancer types, 32 pathways
- **Performance**: 70-100% accuracy with 1-10 shots
- **Top Pathways**: oxphos_program, Jak1_vivo_ko, proliferating
- **Transferability**: 0.5-1.0 similarity scores across cancer types

## Figure References
The manuscript references all five provided figures:
- Figure 1: Dataset Overview (referenced in experiments)
- Figure 2: Pathway Importance Rankings
- Figure 3: Few-Shot Learning Performance
- Figure 4: Cross-Cancer Transferability Matrix
- Figure 5: Biological Validation Results

## Compliance Features
- ✅ NeurIPS formatting (agents4science_2025.sty)
- ✅ 8-page limit (content only)
- ✅ Unlimited references and appendices
- ✅ Complete AI involvement checklist
- ✅ Complete paper checklist
- ✅ Anonymous submission format
- ✅ Professional academic tone
- ✅ Comprehensive experimental validation

## Compilation Status
- ✅ Successfully compiles with pdfLaTeX
- ✅ Generates 13-page PDF (469KB)
- ✅ Includes all figures from provided paths
- ⚠️ Minor warnings for undefined citations (expected for draft)

## Next Steps
1. Review content for accuracy and completeness
2. Add proper bibliography file if needed
3. Finalize author information before submission
4. Conduct final proofreading

The manuscript is ready for review and submission to NeurIPS 2025 Agents4Science track.