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--- |
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language: |
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- en |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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tags: |
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- biology |
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- virology |
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- genomics |
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- pathogenicity |
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- benchmark |
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- viral-genomics |
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size_categories: |
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- 10K<n<100K |
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--- |
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# HVUE: Human Virome Understanding Evaluation |
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## Dataset Description |
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HVUE (Human Virome Understanding Evaluation) is a comprehensive benchmark for evaluating foundation models on viral genomics tasks. The benchmark comprises 7 curated datasets across 3 epidemiologically critical prediction tasks: |
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- **Pathogenicity Classification** (3 datasets) |
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- **Host Tropism Prediction** (1 dataset) |
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- **Transmissibility Assessment** (3 datasets) |
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**Paper**: *HViLM: A Foundation Model for Viral Genomics Enables Multi-Task Prediction of Pathogenicity, Transmissibility, and Host Tropism* |
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**Authors**: Pratik Dutta, Jack Vaska, Pallavi Surana, Rekha Sathian, Max Chao, Zhihan Zhou, Han Liu, and Ramana V. Davuluri |
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**GitHub**: https://github.com/duttaprat/HViLM |
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## Dataset Structure |
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### Pathogenicity Classification |
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**CINI Dataset** |
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- 159 sequences across 4 viral families |
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- Manual literature-based curation |
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- Binary classification: pathogenic vs non-pathogenic |
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**BVBRC-CoV Dataset** |
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- 18,066 coronavirus sequences |
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- Distinguishes human-pathogenic (SARS-CoV-2, MERS-CoV, etc.) from animal-restricted strains |
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**BVBRC-Calici Dataset** |
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- 31,089 calicivirus sequences |
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- Clinical evidence and isolation source-based labels |
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### Host Tropism Prediction |
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**VHDB Dataset** |
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- 9,428 sequences spanning 30 viral families |
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- Binary classification: human-tropic (13.1%) vs non-human-tropic (86.9%) |
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- Experimentally validated host range annotations |
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### Transmissibility Prediction |
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**Coronaviridae Dataset** |
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- ~3,000 coronavirus sequences |
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- R₀-based classification: R₀<1 vs R₀≥1 |
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**Orthomyxoviridae Dataset** |
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- ~2,500 influenza sequences |
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- R₀-based classification |
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**Caliciviridae Dataset** |
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- ~1,800 calicivirus sequences |
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- R₀-based classification |
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## Data Format |
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Each dataset contains three splits: |
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- `train.csv` |
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- `dev.csv` |
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- `test.csv` |
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CSV columns: |
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- `sequence`: Viral genomic sequence (250-1000 bp) |
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- `label`: Binary label (0 or 1) |
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## Usage |
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```python |
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from datasets import load_dataset |
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# Load entire benchmark |
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hvue = load_dataset("duttaprat/HVUE") |
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# Load specific task |
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patho_cini = load_dataset("duttaprat/HVUE", data_files="pathogenicity/CINI/*.csv") |
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# Load specific split |
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train_data = load_dataset("duttaprat/HVUE", data_files="pathogenicity/CINI/train.csv") |
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``` |
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## Citation |
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```bibtex |
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@article{dutta2025hvilm, |
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title={HViLM: A Foundation Model for Viral Genomics Enables Multi-Task Prediction of Pathogenicity, Transmissibility, and Host Tropism}, |
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author={Dutta, Pratik and Vaska, Jack and Surana, Pallavi and Sathian, Rekha and Chao, Max and Zhou, Zhihan and Liu, Han and Davuluri, Ramana V.}, |
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journal={Submitted to RECOMB}, |
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year={2025} |
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} |
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``` |
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## License |
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CC-BY-4.0 |
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## Contact |
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- Pratik Dutta: Pratik.Dutta@stonybrook.edu |
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- GitHub Issues: https://github.com/duttaprat/HViLM/issues |
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