Predicting protein complexes by data integration of different types of interactions
Bibliographic record
Abstract
The explosion of high throughput interaction data from proteomics studies gives us the opportunity to integrate Protein-Protein Interactions (PPI) from different type of interactions. These methods rely on the assumption that proteins within a complex have more interactions across the different data sets which translate into the identification of dense subgraphs. However, the relative importance of the types of interaction are not equivalent in their reliability and accuracy consequently they should be analysed separately. Here we propose a method that use graph theory and mathematical modelling to solve this problem. Our approach has four steps that: i) score independently each type of interaction; ii) build an interaction specific networks for each type; iii) weight the specific networks; and iv) combine and normalise the scores. Using this approach to the BRCA1 Associated genome Surveillance Complex (BASC), we correctly identified the known core components of the complex and subcomplexes that have solved structures as well as predicted new interactions and core complexes. The method presented in this study is of general use. It is flexible enough to allow the development of any scoring system and can be applied to any protein complex to provide the latest knowledge in its interactions and structure.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".