Improving the Protein-Protein Interaction Prediction Engine (PIPE) with Protein Physicochemical Properties
Bibliographic record
Abstract
Protein-protein interactions (PPI) serve an important role in both protein and cell function. They are difficult and time consuming to determine experimentally and thus benefit from in silico prediction methods. This thesis improves a high throughput, sequence-based protein-protein interaction prediction method called the protein-protein interaction engine (PIPE). A Python implementation of the scoring of PIPE was developed. Subsequently, a sequence-based solvent accessibility approach was integrated with PIPE, improving PPI prediction recall by 0.9% at 90% precision. Finally, 166 different sequence-based physicochemical properties were generated using the ProtDCal software tool and were integrated with PIPE using the framework developed in this thesis. The best of these properties improved the recall of PIPE by 2% at 90% precision. This improvement was shown to be statistically significant and was confirmed on a larger test set including 10,000 protein pairs known to interact and 10,000 randomly selected pairs, assumed not to interact.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".