Prediction of Biological Protein-protein Interaction Types Using Short-Linear Motifs
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Bibliographic record
Abstract
Protein-protein interactions (PPIs) play a key role in many biological processes and functions in living cells. Thus, identification, prediction, and analysis of PPIs are important aspects in molecular biology. We propose a computational model to predict biological PPI types using short-linear motifs (SLiMs). The information contained in protein sequences is used to distinguish between interaction types, namely obligate and non-obligate. Classifiers, such as k-nearest neighbor (k-NN), support vector machine (SVM) and linear dimensionality reduction (LDR) on two well-known datasets confirm the power of the proposed model with accuracy above 99%. The results show that the information contained in the training sequences is crucial for prediction and analysis of biological PPIs.
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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 | 0.000 |
| 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 it