# interpro_repeat

Protein-level multi-label dataset sourced from InterPro entry metadata and
UniProtKB protein-to-InterPro cross-references.

## Intended use

Protein repeat annotation prediction from sequence. This evaluates
recovery of curated InterPro classifications, not an experimental assay result.

## Source and labels

- InterPro metadata: `https://ftp.ebi.ac.uk/pub/databases/interpro/current_release/entry.list`
- UniProt REST API: `https://rest.uniprot.org/uniprotkb/stream`
- Card generated (UTC): `2026-10-02`
- Organism Taxonomy ID: `9606` (`all` = all organisms).
- Review status: `reviewed` (`reviewed` = Swiss-Prot, `unreviewed` = TrEMBL, `all` = both).
- UniProt query: `(organism_id:9606) AND (reviewed:true)`
- UniProt TSV fields: `accession,sequence,xref_interpro,organism_id,reviewed`
- InterPro entry type: `Repeat`
- Consider entries observed in at least 5 proteins.
- After splitting, retain entries with at least 20
    positives in training, 3 in validation,
    and 3 in test.
- `targets` is a multi-hot vector ordered by InterPro accession. Accession,
  display name, and type are recorded in `label_vocabulary.json`.
- Proteins without a retained entry before splitting are dropped; after
    support filtering, other split variants may contain all-zero targets.

## Splits

Whole MMseqs2 `easy-linclust` clusters are assigned to splits targeting
`{'train': 0.8, 'validation': 0.1, 'test': 0.1}`, with minimum identity
`0.3`, minimum coverage
`0.8`, and
`1` thread(s). Seed: `1957723`.
When `create_split_subsets` is enabled, pooled random, stratified, and
hold-cluster-out subsets are also included. Hold-cluster-out subsets require
enough MMseqs clusters to populate all three roles.
The optional maximum sequence length is `None` and is
applied before vocabulary construction and MMseqs2 clustering.

## Split sizes

- `test`: 156 rows
- `test_cluster`: 156 rows
- `test_random`: 156 rows
- `test_stratified`: 156 rows
- `train`: 1251 rows
- `train_cluster`: 1251 rows
- `train_random`: 1251 rows
- `train_stratified`: 1251 rows
- `validation`: 156 rows
- `validation_cluster`: 156 rows
- `validation_random`: 156 rows
- `validation_stratified`: 156 rows

Vocabulary size: 10.

## Dataset statistics

[`stats.json`](stats.json) at the dataset root contains row counts by split,
columns, and SeqKit sequence-length metrics.