Literature curation of protein interactions: measuring agreement across major public databases
Bibliographic record
Abstract
Literature curation of protein interaction data faces a number of challenges. Although curators increasingly adhere to standard data representations, the data that various databases actually record from the same published information may differ significantly. Some of the reasons underlying these differences are well known, but their global impact on the interactions collectively curated by major public databases has not been evaluated. Here we quantify the agreement between curated interactions from 15 471 publications shared across nine major public databases. Results show that on average, two databases fully agree on 42% of the interactions and 62% of the proteins curated from the same publication. Furthermore, a sizable fraction of the measured differences can be attributed to divergent assignments of organism or splice isoforms, different organism focus and alternative representations of multi-protein complexes. Our findings highlight the impact of divergent curation policies across databases, and should be relevant to both curators and data consumers interested in analyzing protein-interaction data generated by the scientific community. Database URL: http://wodaklab.org/iRefWeb.
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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 | 0.116 | 0.344 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.056 | 0.045 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".