The BioGRID Interaction Database
Bibliographic record
Abstract
Abstract The goal of the Biological General Repository for Interaction Datasets (BioGRID) (http://www.thebiogrid.org) is to archive and freely disseminate collections of genetic and protein interactions from major model organisms. BioGRID currently houses over 335,000 interactions curated from high-throughput datasets and individual focused studies found in the primary literature, as derived from some 23,000 publications. Complete coverage of the entire literature for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe has been achieved, resulting in the curation of over 246,000 interactions, and efforts to expand curation across multiple species are underway. Through collaborations with the Gene Ontology (GO) Consortium and the Linking Animal Models to Human Disease Initiative (LAMHDI), we are focusing our curation efforts across model organisms on particular areas of biology to enable insights into conserved networks and pathways that are relevant to human health.The BioGRID 3.0 web interface contains new search and display features that enable rapid queries across multiple data types and sources. A dedicated Interaction Management System (IMS) is used to track all curation and to prioritize publications across multiple curation projects. BioGRID data are incorporated in several model organism databases and other biological databases. The entire BioGRID interaction collection may be downloaded in multiple file formats, including PSI MI XML, and source code for BioGRID is freely available without any restrictions. This work is supported by NIH NCRR grant R01 RR024031 to MT and KD, and by grants from the CIHR and BBSRC to MT.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.086 | 0.106 |
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".