Component puzzle protein–protein interaction prediction
Bibliographic record
Abstract
Proteins primarily perform their functions through interactions with other proteins, making the accurate prediction of protein-protein interactions (PPIs) a fundamental problem. Experimental methods for determining PPIs are often slow and expensive, which has driven significant efforts to improve the performance of computational methods in this field. While many methods have been designed, recent thorough investigations proved that the existing methods learn exclusively from sequence similarities and node degrees. When such data leakage is avoided, performances were shown to become random. We introduce C3PI, a novel sequence-based deep learning framework designed for predicting PPIs. C3PI uses as input ProtT5 protein embeddings into a complex architecture that includes two novel components, a puzzler and an entangler, which significantly enhance the model's performance. Through extensive comparisons with state-of-the-art methods across many datasets, C3PI consistently outperforms competing approaches, especially in key metrics such as AUPRC and AUROC. Most importantly, C3PI is the first PPI prediction method to achieve a significant improvement over random on the leakage-free gold standard dataset. C3PI is available as a web server at c3pi.csd.uwo.ca and source code from github.com/lucian-ilie/C3PI.
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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".