Identifying Protein Complexes Based on Multiple Topological Structures in PPI Networks
Bibliographic record
Abstract
Various computational algorithms are developed to identify protein complexes based on only one of specific topological structures in protein-protein interaction (PPI) networks, such as cliques, dense subgraphs, core-attachment structures and starlike structures. However, protein complexes exhibit intricate connections in a PPI network. They cannot be fully detected by only single topological structure. In this paper, we propose an algorithm based on multiple topological structures to identify protein complexes from PPI networks. In the proposed algorithm, four single topological structure based algorithms are first employed to identify raw predictions with specific topological structures, respectively. Those raw predictions are trimmed according to their topological information or GO annotations. Similar results are carefully merged before generating final predictions. Numerical experiments are conducted on a yeast PPI network of DIP and a human PPI network of HPRD. The predicted results show that the multiple topological structure based algorithm can not only obtain a more number of predictions, but also generate results with high accuracy in terms of f-score, matching with known protein complexes and functional enrichments with GO.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".