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README.md

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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, ~50,000 sequences)
+- **Host Tropism Prediction** (1 dataset, 9,428 sequences)
+- **Transmissibility Assessment** (3 datasets, ~7,300 sequences)
+
+**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` (70%)
+- `dev.csv` (15%)
+- `test.csv` (15%)
+
+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")
+```
+
+## Data Leakage Prevention
+
+Systematic overlap analysis was performed between pre-training data (VIRION database) and HVUE benchmark:
+- Method: Accession ID matching + MMseqs2 similarity (>95% identity)
+- Result: 186 overlapping sequences removed from pre-training corpus
+- Clean separation between pre-training and evaluation data
+
+## 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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