Bibliographic record
Abstract
Abstract Compositionally biased regions (BRs) occur when a few amino‐acid types are enriched in a protein segment. There are possibly BR types in the known protein universe that have not been characterized experimentally. The UniProt protein database has been surveyed for evidence of such compositionally ‘‘dark matter’’. A ‘‘dark biased region’’ (DBR) is defined as a biased region with low probability of being an individual structural domain or intrinsically disordered region. The bias annotation program fLPS is used to generate a list of >13 million BRs, which is then thoroughly filtered for structure and intrinsic disorder. About a third of BRs (31%) has both substantial intrinsic disorder and structure. After filtering, there are ≈0.9 million DBRs (≈7% of the original BRs in ≈1.4% of proteins). These DBRs are hugely enriched in eukaryotes and hugely depleted in bacteria. They tend to be more hydrophobic than other protein regions, but are made of less extreme combinations of hydrophobic/hydrophilic residues. Given varying assumptions, It has been estimated that how many DBRs there might be for the high bias levels examined (with p ‐values < 1 × 10 –06 ), deriving a reasonable range of 0.7–7.2% of proteins having such DBRs. Hypotheses are examined about what such DBRs might be, that is, that they are from un‐ or undersampled domain/region categories or are unappreciated categories somewhat like existing ones.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".