Improving energetic feature selection to classify protein: protein interactions
Bibliographic record
Abstract
Protein-protein interactions (PPIs) are known for its important role in diverse biological processes. One of the crucial issues to understand and classify PPI is to characterize their interfaces in order to discriminate between transient and permanent complexes. The stability of protein-protein interactions depends on the energetic features of interaction surfaces. This work explores the surfaces of complex interaction classified as permanent and transient, in order to find those energetic features that can differentiate between both type of complexes. We claim that the number of energetic features and their contribution to the interactions can be key factors to predict between transient and permanent interactions. Moreover, the features used can be adjusted according to the size of the complex studied. We evaluate different classifiers to predict these interactions, using a set of 298 complexes extracted from databases of protein complexes---in terms of their known three-dimensional structure---, and which were already classified as transient or permanent. As a result, we obtained an improved accuracy up to 86.6% when using SVM with kernel linear.
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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 | 0.001 |
| 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".