FAC-PIN: Fast agglomerative clustering method for functional modules and protein complex identification in PINs
Bibliographic record
Abstract
Proteins are known to interact with each other to perform living organism functions by forming functional modules or protein complexes. Many community detection methods have been devised for the discovery of functional modules or protein complexes from protein interaction networks. One common problem in current agglomerative community detection approaches is that vertices with just one neighbor are often classified as separated clusters, which does not make sense biologically. Also, a major limitation of agglomerative techniques is that their computational efficiency do not scale well to large protein interaction networks (PINs). In this paper, we propose a new agglomerative algorithm, FAC-PIN, based on a local premetric of relative vertex-to-vertex clustering value and which addresses the above two issues. Our proposed FAC-PIN method has been applied to PIN of baker's yeast for validating functional modules and protein complexes. The preliminary computational results show that FAC-PIN can discover functional modules and protein complexes from PINs more accurately. As well as, we have also compared the computational times with HC-PIN and CNM algorithms. Our algorithm outperformed above algorithms. Our FAC-PIN algorithm is faster and accurate algorithm which is the current state-of-the-art agglomerative approach to complex prediction and functional module identification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".