Sequence-based Protein Interaction Site Prediction using Computer Vision and Deep Learning
Bibliographic record
Abstract
Computational prediction of protein-protein interaction (PPI) from protein sequence is important as many cellular functions are made possible through PPI.The Protein Interaction Prediction Engine (PIPE) software suite was developed at Carleton University for such predictions.This thesis aims to conduct a thorough performance assessment of the PIPE-Sites predictor through the use of a large high-quality set of known PPI sites.The results show that PIPE-Sites has relatively low accuracy even after retuning the inherent hyperparameters of the method.Furthermore, PIPE-Sites are shown to be ineffective when applied to similarity-weighted score data.Thus, three new sequence-based methods of predicting PPI sites are proposed and evaluated, including the Panorama, BrightSpot, and ClusterNet methods.The new methods leverage similarityweighted score data to further increase performance.Ultimately, ClusterNet significantly outperforms the other methods over two different performance metrics when evaluated on both human and yeast data PPI site data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".