BioGRID Organism Binary Protein-Protein Interactions
Bibliographic record
Abstract
Balanced datasets compiled from BioGRID Release 4.4.198 (May 25, 2021) organism data. Provides new benchmarks containing recent data to avoid discrepancies found in older benchmark datasets. Filtering approach: Only physical interactions with high/low throughput are included with the following detection methods: two-hybrid affinity capture-ms affinity capture-western reconstituted complex affinity capture-luminescence co-crystal structure far western fret protein-peptide co-localization affinity capture-rna co-purification Interaction confidence level: Confidence: ( "c0"): all non-redundant interactions listed by BioGRID have been included. High confidence: ( "c1"): only interactions listed by BioGRID multiple times have been included. Very high confidence ("c2"): only interactions listed by BioGRID with multiple different publication sources have been included. Proteins with >40% sequence identity are removed. Negative interactions were generated by random sampling of proteins in positive interactions such that the negative pairs are not found in the positive interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.043 | 0.062 |
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".