README.md

---
annotations_creators: []
language:
- en
multilinguality: []
pretty_name: Target-QA
size_categories:
- n<1K
source_datasets:
- depmap
- biomedgraphica
tags:
- bioinformatics
- graph-ml
- precision-medicine
task_categories:
- question-answering
- text-generation
task_ids:
- extractive-qa
- text2text-generation
paperswithcode_id: null
configs:
  - config_name: train
    data_files: train_samples_detailed.csv

  - config_name: test
    data_files: test_samples_detailed.csv
---


# 🎯 Target-QA: The First QA Dataset Benchmarking Target Priorization Based on DepMap

<div align="center" style="line-height: 1.4;">

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  <a href="https://github.com/FuhaiLiAiLab/GALAX" target="_blank" style="margin: 2px;">
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  <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;"/>
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</div>

---

## 📑 Dataset Summary  

**Target-QA** is derived from the **DepMap** multi-omics and CRISPR screening cohorts, harmonized via **[BioMedGraphica](https://huggingface.co/datasets/FuhaiLiAiLab/BioMedGraphica)**.  
It enables **multi-modal reasoning** by combining numeric evidence, topological knowledge and language context for **CRISPR target prioritization**.  

This dataset supports the training and benchmarking of **graph-augmented large language models (LLMs)**, such as **[GALAX](https://huggingface.co/FuhaiLiAiLab/GALAX)**, for reasoning across structured and unstructured biomedical information.  
- 🔗 Source code: [GitHub](https://github.com/FuhaiLiAiLab/GALAX)  
- 🤗 Model parameters: [Hugging Face](https://huggingface.co/FuhaiLiAiLab/GALAX)  

---

## 🛠️ Preprocessing Details  

![Dataset composition and splits](https://github.com/FuhaiLiAiLab/GALAX/blob/main/Figures/FigureS1.png?raw=true)

- **Starting cohort**: 985 DepMap cell lines  
  - 649 annotated (cancerous)  
  - 336 non-annotated or non-cancerous  
- **Target-QA subset**: 363 overlapping with CRISPR gene effect data  
- **Omics modalities integrated into 834,809 entities**:  
  - Promoter: 86,238  
  - Gene: 86,238  
  - Transcript: 412,039  
  - Protein: 121,419  
- **Knowledge graph integration**:  
  - Protein–protein interactions: 17,151,453 edges  
  - Disease–target associations: 27,087,971 edges  

---

## 📂 Data Splits  

- **Pretraining set**: 336 samples  
  - Train: 269  
  - Test: 67  
- **Target-QA set**: 363 samples  
  - Train: 300  
  - Test: 63  

**Test distribution includes:** LUAD (7), BRCA (6), COAD/READ (5), PAAD (4), GBM (3), SARC (3), OV (3), SKCM (3), ESCA (3), SCLC (3), HNSC (2), LUSC (2), STAD (2), etc.  

---

## 🧪 Supported Tasks & Benchmarks  

- **Graph-Augmented QA**: Identify CRISPR targets from omics + KG context  
- **Knowledge Graph Reasoning**: Subgraph extraction & signaling network prioritization  
- **Multi-Omic Target Prioritization**: Integrated of epigenomic, genomic, transcriptomic and proteomic

---

## 📊 Dataset Structure  

### Example JSON  

```json
{
  "cell_line_name": "GAMG",
  "cell_line_id": "ACH-000098",
  "disease": "glioblastoma",
  "disease_bmgc_id": "BMGC_DS00965",
  "sample_dti_index": 123,
  "input": {
    "top_k_gene": {
      "hgnc_symbols": ["EGFR", "CDKN2A"],
      "protein_bmgc_ids": ["BMGC_PR01234"],
      "protein_llmname_ids": ["ENSP00000354587"]
    },
    "top_k_transcript": {...},
    "top_k_protein": {...},
    "knowledge_graph": {
      "disease_protein": {...},
      "ppi_neighbors": {...},
      "protein_relationships": ["BRCA1 → TP53"]
    }
  },
  "ground_truth_answer": {
    "hgnc_symbols": ["TP53", "EGFR"],
    "protein_bmgc_ids": ["BMGC_PR00987", "BMGC_PR04567"],
    "protein_llmname_ids": ["ENSP00000439978"]
  }
}
```

## 📝 Prompt Design  

### Initial Prompt (`P_init_n`)  

**Inputs:**  
- Top-10 ranked genes, transcripts, proteins  
- Disease-associated proteins from KG  
- Known PPI and disease–protein relationships  

**Output:**  
- 100 vulnerability genes `r_init[n,1...100]`  

### Refined Prompt (`P_final_n`)  

**Inputs:**  
- Same as above  
- Subsignaling gene regulatory network (from graph generator)  

**Output:**  
- Refined 100 vulnerability genes `r_hat[n,1...100]`  

---

## ⚖️ Licensing  

This dataset is based on **DepMap** data and is subject to the **DepMap Terms of Use**:  
- Free for **research purposes only**  
- **Commercial use prohibited** without explicit Broad Institute license  
- ML models may be trained for **internal research use** or shared for **non-profit research**  
- Attribution to **Broad Institute / DepMap** is required  

🔗 [DepMap Terms of Use](https://depmap.org/portal)  

---

## 📚 Citation  

If you use **Target-QA** or **GALAX**, please cite:  

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