A model based on minimotifs for classification of stable protein-protein complexes
Bibliographic record
Abstract
Prediction of protein-protein interactions (PPIs) is an important problem in biology, since interactions play key role in most biological processes and functions in living cells. PPIs have been studied from many perspectives. Of these, an important problem is prediction of different complex types such as obligate vs. non obligate and transient vs. permanent, among others. We focus on prediction of obligate protein complexes, which are more stable and perform a specific function, as opposed to transient and non-obligate complexes which last for a short period of time. We have modeled the prediction problem using minimotifs, aka short-linear motifs, to extract information contained in the protein sequences to distinguish between obligate and non-obligate PPIs. Incorporating different classifiers such as the k-nearest neighbor (k-NN), the support vector machine (SVM) and linear dimensionality reduction (LDR) yields a very powerful scheme for prediction. On two well-known datasets, the model delivers classification accuracies as high as 99%. Analysis and cross-dataset validation show that the information contained in the training sequences is crucial for prediction and determination of stability in PPIs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".