---
language: en
license: mit
tags:
  - graph-ml
  - bioinformatics
  - precision-medicine
  - explainable-ai
  - reinforcement-learning
datasets:
  - FuhaiLiAiLab/Target-QA
library_name: transformers
pipeline_tag: text-generation
model-index:
  - name: GALAX
    results:
      - task:
          type: text-generation
          name: Target Prioritization
        dataset:
          name: Target-QA
          type: FuhaiLiAiLab/Target-QA
        metrics:
          - type: precision
            value: 0.5472
          - type: recall
            value: 0.5332
          - type: hit@10
            value: 0.8815
          - type: hit@5
            value: 0.9249
---

# GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine

<div align="center">
  <img src="https://github.com/FuhaiLiAiLab/GALAX/blob/main/Figures/GALAX-logo.png?raw=true" width="40%" alt="GALAX" />
</div>

<div align="center" style="line-height: 1;">
  <!-- GitHub -->
  <a href="https://github.com/FuhaiLiAiLab/GALAX" target="_blank" style="margin: 2px;">
    <img alt="GitHub" src="https://img.shields.io/badge/GitHub-GALAX%20Code-181717?logo=github&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>

  <!-- Hugging Face Model -->
  <a href="https://huggingface.co/FuhaiLiAiLab/GALAX" target="_blank" style="margin: 2px;">
    <img alt="Hugging Face Model" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-GALAX%20Model-ffc107?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>

  <!-- Hugging Face Dataset -->
  <a href="https://huggingface.co/datasets/FuhaiLiAiLab/Target-QA" target="_blank" style="margin: 2px;">
    <img alt="Hugging Face Dataset" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Target--QA%20Dataset-ff6f61?color=ffc107&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>

<div align="center" style="line-height: 1;">
  <!-- arXiv -->
  <a href="https://arxiv.org/abs/2509.20935" target="_blank" style="margin: 2px;">
    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-GALAX%20Paper-b31b1b?logo=arxiv&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>

  <!-- License -->
  <a href="LICENSE" style="margin: 2px;">
    <img alt="License" src="https://img.shields.io/badge/License-MIT-0a4d92?logo=open-source-initiative&logoColor=white" style="display: inline-block; vertical-align: middle;"/>
  </a>
</div>

---

## 🧩 Model Overview

![GALAX Overall Architecture](https://github.com/FuhaiLiAiLab/GALAX/blob/main/Figures/Figure3.png?raw=true)

**GALAX** is a graph-augmented language model designed for explainable target prioritization in precision medicine. It combines three key components:  
- **LLaMA3-8B-Instruct** as the language backbone, further adapted with the BioMedGraphica corpus and fine-tuned on Target-QA.  
- **Graph Attention Network (GAT)** pretrained on integrated multi-omics data and BioMedGraphica knowledge graphs.  
- **A reinforcement-guided subgraph generator** that enables interpretable reasoning by constructing biologically meaningful subgraphs from multi-omics and knowledge graph signals.  

By jointly leveraging **multi-omics features**, **protein–protein interactions**, and **disease–target associations**, GALAX provides an interpretable framework for **CRISPR target prioritization** across diverse cancer cell lines. To support benchmarking and reproducibility, we also introduce the **[Target-QA dataset](https://huggingface.co/datasets/FuhaiLiAiLab/Target-QA)**.

---

## 🚀 How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from huggingface_hub import snapshot_download
import os, torch

# 1. Load GALAX language model
model_id = "FuhaiLiAiLab/GALAX"
tokenizer = AutoTokenizer.from_pretrained(model_id)
lm_model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

# 2. Access graph foundation model
repo_path = snapshot_download(model_id)
combined_model_path = os.path.join(repo_path, "best_combined_model.pt")
device = "cuda" if torch.cuda.is_available() else "cpu"
best_combined_model = torch.load(combined_model_path, map_location=device)
```

---

## ⚙️ Experimental Setup

- **Backbone LM:** LLaMA3-8B-Instruct (QA-tuned).  
- **Graph Encoder:** BioBERT-v1.1 embeddings + GAT with edge masking.  
- **Training:** Adam optimizer on 2× NVIDIA H100 (80GB).  
- **Top features per omics modality:** K = 10.  
- **Subgraph rollout depth:** L = 5, candidate nodes η = 20.  
- **Evaluation:** Precision, Recall, F1, Jaccard, Hit@5, Hit@10.  

---


## 📊 Results

GALAX consistently outperforms baselines and ablation variants.

- **Overall Precision:** 0.5472  
- **Overall Recall:** 0.5332  
- **Hit@10:** 0.8815  
- **Hit@5:** 0.9249  

**Table 1. Precision and Recall across datasets**

| Model                  | Overall Precision ↑ | Overall Recall ↑ | LUAD Precision ↑ | LUAD Recall ↑ | BRCA Precision ↑ | BRCA Recall ↑ |
|-------------------------|---------------------|------------------|------------------|---------------|------------------|---------------|
| M2T                     | 0.0016              | 0.0011           | 0.0020           | 0.0014        | 0.0000           | 0.0000        |
| GAT                     | 0.0006 ± 0.0000     | 0.0006 ± 0.0000  | 0.0000 ± 0.0000  | 0.0000 ± 0.0000 | 0.0033 ± 0.0000 | 0.0033 ± 0.0000 |
| L3 + Omics              | 0.0071 ± 0.0032     | 0.0013 ± 0.0002  | 0.0079 ± 0.0137  | 0.0005 ± 0.0008 | 0.0020 ± 0.0035 | 0.0017 ± 0.0029 |
| L3 + Omics + KG         | 0.0125 ± 0.0032     | 0.0029 ± 0.0003  | 0.0014 ± 0.0025  | 0.0010 ± 0.0017 | 0.0073 ± 0.0068 | 0.0033 ± 0.0029 |
| L3-FT(Med) + Omics      | 0.0179 ± 0.0045     | 0.0133 ± 0.0064  | 0.0091 ± 0.0018  | 0.0105 ± 0.0044 | 0.0110 ± 0.0086 | 0.0106 ± 0.0075 |
| L3-FT(Med) + Omics + KG | 0.0158 ± 0.0030     | 0.0058 ± 0.0011  | 0.0081 ± 0.0071  | 0.0024 ± 0.0017 | 0.0149 ± 0.0057 | 0.0050 ± 0.0000 |
| L3-FT(QA) + Omics       | 0.5250 ± 0.0282     | 0.4959 ± 0.0435  | 0.5201 ± 0.0408  | 0.4905 ± 0.0532 | 0.5074 ± 0.0498 | 0.4856 ± 0.0570 |
| L3-FT(QA) + Omics + KG  | 0.5185 ± 0.0240     | 0.4908 ± 0.0402  | 0.5214 ± 0.0242  | 0.4952 ± 0.0432 | 0.4856 ± 0.0395 | 0.4656 ± 0.0436 |
| G-Retriever + pre-GAT   | 0.4763 ± 0.0004     | 0.3929 ± 0.0063  | 0.4642 ± 0.0181  | 0.3881 ± 0.0264 | 0.4414 ± 0.0099 | 0.3772 ± 0.0010 |
| **GALAX**               | **0.5472 ± 0.0053** | **0.5332 ± 0.0031** | **0.5345 ± 0.0185** | **0.5157 ± 0.0043** | **0.5608 ± 0.0031** | **0.5533 ± 0.0033** |

**Table 2. Hit@10 and Hit@5 across datasets**

| Model                  | Overall Hit@10 ↑ | Overall Hit@5 ↑ | LUAD Hit@10 ↑ | LUAD Hit@5 ↑ | BRCA Hit@10 ↑ | BRCA Hit@5 ↑ |
|-------------------------|------------------|-----------------|---------------|--------------|---------------|--------------|
| M2T                     | 0.0029           | 0.0000          | 0.0000        | 0.0000       | 0.0000        | 0.0000       |
| GAT                     | 0.0000 ± 0.0000  | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 |
| L3 + Omics              | 0.0021 ± 0.0037  | 0.0032 ± 0.0055 | 0.0048 ± 0.0082 | 0.0095 ± 0.0165 | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 |
| L3 + Omics + KG         | 0.0122 ± 0.0033  | 0.0085 ± 0.0037 | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 | 0.0056 ± 0.0096 | 0.0111 ± 0.0192 |
| L3-FT(Med) + Omics      | 0.0122 ± 0.0072  | 0.0116 ± 0.0097 | 0.0000 ± 0.0000 | 0.0000 ± 0.0000 | 0.0111 ± 0.0192 | 0.0000 ± 0.0000 |
| L3-FT(Med) + Omics + KG | 0.0132 ± 0.0040  | 0.0106 ± 0.0048 | 0.0048 ± 0.0082 | 0.0095 ± 0.0165 | 0.0111 ± 0.0192 | 0.0000 ± 0.0000 |
| L3-FT(QA) + Omics       | 0.8693 ± 0.0157  | 0.8889 ± 0.0168 | 0.8667 ± 0.0218 | 0.8476 ± 0.0165 | 0.8389 ± 0.0096 | 0.8889 ± 0.0509 |
| L3-FT(QA) + Omics + KG  | 0.8529 ± 0.0153  | 0.8794 ± 0.0114 | 0.8048 ± 0.0541 | 0.7905 ± 0.0436 | 0.8222 ± 0.0347 | 0.8778 ± 0.0192 |
| G-Retriever + pre-GAT   | 0.8550 ± 0.0046  | 0.8804 ± 0.0037 | 0.8524 ± 0.0165 | 0.8857 ± 0.0000 | **0.8667 ± 0.0000** | 0.8667 ± 0.0000 |
| **GALAX**               | **0.8815 ± 0.0033** | **0.9249 ± 0.0048** | **0.8810 ± 0.0082** | **0.9238 ± 0.0436** | 0.8500 ± 0.0441 | **0.8889 ± 0.0839** |

---

## 🔬 Intended Uses

- **Research use only**  
- Benchmarking **graph-language foundation models** in target priorization
- Target prioritization in **cancer biology**

---

## 📜 Citation

If you use this model, please cite:

```bibtex
@article{zhang2025galax,
  title     = {GALAX: Graph-Augmented Language Model for Explainable Reinforcement-Guided Subgraph Reasoning in Precision Medicine},
  author    = {Zhang, Heming and Huang, Di and Li, Wenyu and Province, Michael and Chen, Yixin and Payne, Philip and Li, Fuhai},
  journal   = {arXiv preprint arXiv:2509.20935},
  year      = {2025},
  doi       = {10.48550/arXiv.2509.20935},
  url       = {https://arxiv.org/abs/2509.20935}
}