File size: 2,358 Bytes
341613d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 | # 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): `2026-10-05`
- 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: 9.
## Dataset statistics
[`stats.json`](stats.json) at the dataset root contains row counts by split,
columns, and SeqKit sequence-length metrics.
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