Zero-Shot Protein Segmentation (ZPS) Data and Embeddings
Bibliographic record
Abstract
uniprotkb_Human.txt this is a raw text file that contains a downloaded copy of UniProtKB this inlcudes all reviewed human protein sequences we used annotations from this file to copmare to ZPS predictions uniprotkb_Human_Sequences.fasta this is a fasta file that contains reviewed human protein sequences these are the sequences we used as input to ProtT5 to generate protein embeddings ZPS_Boundaries.tsv this is a tab separated file that contains the boundaries of protein segments defined by ZPS for reviewed human protein sequences we used zero-based indexing for the protein boundaries ZPS_Segment_Embeddings.hdf5 this is a hdf5 file that contains segment embeddings for the human proteome see "Zero-shot segmentation using embeddings from a language model identifies functional regions in the human proteome" A. G. Sangster 2025 for definition of segment embeddings segment boundaries in this file are also in zero-based indexing evaluation_data.zip includes: disprot_functional_annotations.tsv this conatins DisProt annotations that are labeled as "molecular_function" or "disorder_function" for the human proteome this is from the 2025-06 DisProt release disprot_functional_annotations_per_segment.tsv this is a parsed version of disprot_functional_annotations.tsv this includes protein segment keys and their corresponding disprot functional annotations these labels were used for multi-label evaluations protGPS_dataset.csv this is a copy of the dataset provided on DOI 10.5281/zenodo.14795444 in notebook/dataset.csv ProtGPS_idmapping_2025_08_13.tsv this is the ID mapping data downloaded from UniProt to map gene names and UniProt IDs found in protGPS_dataset.csv to UniProt IDs used in ZPS protGPS_data_only_disordered_segments.tsv this is the parsed version of protGPS_dataset.csv this includes protein IDs, train/dev/test split, a list of labels attributed to the protein, and a list of segment keys that over-lap with MobiDB disorder annotations these labels were used for multi-label evaluations uniprot_annotations_per_segment_multi-class.tsv this is a parsed version of uniprotkb_Human.txt this includes protein segment keys, protein IDs, gene IDs, and labels used in multi-class evaluations multi-class labels include: PROSITE_LABELS: labels of the top ~20 most commonly occuring protein domains as annotated by ProRule on UniProt IDR_VS_DOMAIN_LABELS: labels include Disordered (as annotated by MobiDB via UniProt), ProRule (as annotated by ProRule via UniProt, indicating domain), and Background (does not overlap with a MobiDB disorder annotation or a ProRule domain annotation) COMP_BIAS_LABLES: labels for compositional bias annotation (as annotated by MobiDB via UniProt) DISORDER_LABELS: for segments that overlap with a MobiDB disordered annotation (via UniProt), take the name of the other overlapping annotation with the highest IoU uniprot_annotations_per_segment_multi-label.tsv this is a parsed version of uniprotkb_Human.txt this includes protein segment keys and labels used in multi-label evaluations Protein segment keys: are formatted as "UniProtID start-stop", where start and stop positions reference the canonical protein sequence on UniProt and use zero-based indexing. *see "Zero-shot segmentation using embeddings from a language model identifies functional regions in the human proteome" (A. G. Sangster 2025) on how annotations were transfered to protein segments
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.071 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".