File size: 2,751 Bytes
37925cf 98cd47f 37925cf 98cd47f 37925cf 98cd47f 37925cf 98cd47f 37925cf 98cd47f 37925cf | 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 65 66 67 68 69 70 71 72 | # 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): `2026-10-07`
- 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. Because validation and test
support select the entries, scores are conditional on that label selection.
- `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 are dropped, both before splitting and
again after split-support filtering, so no row has all-zero targets.
- `id` is the UniProtKB accession of the first entry carrying each unique sequence.
## Splits
Whole MMseqs2 clusters are assigned to splits targeting
`{'train': 0.8, 'validation': 0.1, 'test': 0.1}`. Seed: `1957723`. Clustering settings:
- MMseqs workflow: `easy-cluster`
- Minimum sequence identity (`--min-seq-id`): `0.3`
- Minimum coverage (`-c`): `0.8`
- Coverage mode (`--cov-mode`): `1`
- Threads: `1`
- Other MMseqs2 options use MMseqs2 defaults.
When `create_split_subsets` is enabled, pooled random, stratified (by each
protein's globally rarest positive entry), 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`: 116 rows
- `test_cluster`: 116 rows
- `test_random`: 116 rows
- `test_stratified`: 116 rows
- `train`: 850 rows
- `train_cluster`: 850 rows
- `train_random`: 850 rows
- `train_stratified`: 850 rows
- `validation`: 118 rows
- `validation_cluster`: 118 rows
- `validation_random`: 118 rows
- `validation_stratified`: 118 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.
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