Update README.md

README.md

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 ![GALAX Overall Architecture](./Figure3.png)
 
-**GALAX** is a graph-augmented language model that integrates:  
-- **LLaMA3-8B-Instruct** as the language backbone (Target-QA tuned).  
-- **Graph Attention Network (GAT)** trained on BioMedGraphica (multi-omics + knowledge graph).  
-- **Reinforcement-guided subgraph generator** for interpretable target prioritization.  
+**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.  
 
-The model unifies **multi-omics features** with **protein–protein interactions** and **disease–target associations**, enabling **explainable CRISPR target prioritization** across cancer cell lines.
+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)**.
 
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