# Virus Host Prediction Eval

> Evaluates computational tools and genomic features for predicting prokaryotic virus-host interactions. It probes the ability of models to correctly link viral sequences to their host taxa using either pairwise link prediction or taxonomic classification formulations. Use when the user wants to benchmark on RefSeq-VHDB, MetaHiC-VHDB, or asks about evaluating this task. Reports Top-1 accuracy.

- Skill: `qhjqhj00/virus-host-prediction-eval` (Agent Skill)
- Install (CLI): `npx skillmds add qhjqhj00/virus-host-prediction-eval`
- Raw SKILL.md: https://api.skillmd.com/api/skills/qhjqhj00/virus-host-prediction-eval/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Productivity
- Author: qhjqhj00 (https://skillmd.com/u/qhjqhj00)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/qhjqhj00/virus-host-prediction-eval

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# virus-host-prediction-eval

> Computational approaches for virus host prediction: A review of methods and applications — Shang et al. (2025) (arXiv:2509.00349, 2025)

## What this evaluates

Evaluates computational tools and genomic features for predicting prokaryotic virus-host interactions. It probes the ability of models to correctly link viral sequences to their host taxa using either pairwise link prediction or taxonomic classification formulations.

## Datasets

- **RefSeq-VHDB** — total 4698; splits: test (4698)
- **MetaHiC-VHDB** — total ?; splits: test (-1)

## Metrics

- `Top-1 accuracy` **(primary)** — range: [0, 1]
  - The proportion of viruses for which the single highest-scoring predicted host matches the true host label.
- `Top-N accuracy` — range: [0, 1]
  - The proportion of viruses for which the true host appears anywhere in the top-N predicted hosts.
- `alignment proportion` — range: [0, 1]
  - The fraction of total viruses that yield at least one valid alignment or prediction.
- `correct match ratio` — range: [0, 1]
  - The ratio of correctly predicted host hits to the total number of alignment hits returned, estimating errors from Top-N predictions.

## Input / output format

**Input**: Viral genomic sequence, optionally paired with candidate host genomes/MAGs or specific biological features (CRISPR spacers, prophage regions).

**Output**: Link prediction: probability score p ∈ [0,1] per virus-host pair. Classification: single taxonomic label (e.g., genus/family). Feature evaluation: list of alignment hits with pid and coverage/length.

## Scoring recipe

```python
def evaluate(predictions, gold, top_n=5):
    top1_correct = sum(1 for p, g in zip(predictions, gold) if p == g)
    top1_acc = top1_correct / len(gold)
    topn_correct = sum(1 for p_list, g in zip(predictions, gold) if g in p_list[:top_n])
    topn_acc = topn_correct / len(gold)
    aligned = sum(1 for p in predictions if len(p) > 0)
    align_prop = aligned / len(gold)
    total_hits = sum(len(p) for p in predictions)
    correct_hits = sum(1 for p_list, g in zip(predictions, gold) for p in p_list if p == g)
    correct_ratio = correct_hits / total_hits if total_hits > 0 else 0
    return top1_acc, topn_acc, align_prop, correct_ratio
```

## Common pitfalls

- Extreme class imbalance in link prediction causes high false positive rates without stringent alignment thresholds.
- Top-N accuracy can be misleadingly high while yielding a low correct match ratio due to many incorrect hits.
- Closed-set classification cannot predict hosts from taxa absent in the training or reference database.
- Threshold sensitivity: minor changes in percentage identical matches (pid) drastically trade off accuracy against alignment proportion.

## Evidence (verbatim from paper)

> The performance was quantified using four metrics (see Methods): Top-1 accuracy (accuracy on the best hit), Top-N accuracy (accuracy on all alignment), alignment proportion (proportion of aligned viruses), and correct match ratio (estimate the errors introduced by using Top-N accuracy).

## Citation

```bibtex
@misc{shang2025computational,
  title={Computational approaches for virus host prediction: A review of methods and applications},
  author={Shang et al. (2025)},
  year={2025},
  note={arXiv:2509.00349}
}
```

- arXiv: 2509.00349