PIPs: human protein-protein interaction prediction database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The PIPs database (http://www.compbio.dundee.ac.uk/www-pips) is a resource for studying protein-protein interactions in human. It contains predictions of >37,000 high probability interactions of which >34,000 are not reported in the interaction databases HPRD, BIND, DIP or OPHID. The interactions in PIPs were calculated by a Bayesian method that combines information from expression, orthology, domain co-occurrence, post-translational modifications and sub-cellular location. The predictions also take account of the topology of the predicted interaction network. The web interface to PIPs ranks predictions according to their likelihood of interaction broken down by the contribution from each information source and with easy access to the evidence that supports each prediction. Where data exists in OPHID, HPRD, DIP or BIND for a protein pair this is also reported in the output tables returned by a search. A network browser is included to allow convenient browsing of the interaction network for any protein in the database. The PIPs database provides a new resource on protein-protein interactions in human that is straightforward to browse, or can be exploited completely, for interaction network modelling.
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.
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 | 0.001 | 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 | 0.001 | 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 it