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