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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".