metadata
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
📑 Dataset Summary
Target-QA is derived from the DepMap multi-omics and CRISPR screening cohorts, harmonized via 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, for reasoning across structured and unstructured biomedical information.
- 🔗 Source code: GitHub
- 🤗 Model parameters: Hugging Face
🛠️ Preprocessing Details
- 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
{
"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
📚 Citation
If you use Target-QA or GALAX, please cite:
@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}
}