The Hera database and its use in the characterization of endoplasmic reticulum proteins
Bibliographic record
Abstract
MOTIVATION: Information concerning endoplasmic reticulum (ER) proteins is widely dispersed and cannot be easily and rapidly processed by the biological community. We present a comprehensive database of human ER proteins, called Human ER Aperçu (Hera). The Hera database was constructed by exhaustively searching through public databases and the scientific literature for ER proteins. RESULTS: Hera was used for the analysis of characteristics common to all human ER proteins. Our results show that a high proportion of ER proteins (59%) have at least one transmembrane domain and display physical characteristics consistent with this observation. In addition, one-third of ER proteins contain known ER retrieval or retention signals and 70% of ER proteins contain a signal peptide or anchor. Finally, 85% of ER proteins contain at least one InterPro motif. The most abundant InterPro motifs in ER proteins represent many of the most well-characterized functions of the ER.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".