Identifying Protein-Protein Interaction using Tree LSTM and Structured\n Attention
Why this work is in the frame
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Bibliographic record
Abstract
Identifying interactions between proteins is important to understand\nunderlying biological processes. Extracting a protein-protein interaction (PPI)\nfrom the raw text is often very difficult. Previous supervised learning methods\nhave used handcrafted features on human-annotated data sets. In this paper, we\npropose a novel tree recurrent neural network with structured attention\narchitecture for doing PPI. Our architecture achieves state of the art results\n(precision, recall, and F1-score) on the AIMed and BioInfer benchmark data\nsets. Moreover, our models achieve a significant improvement over previous best\nmodels without any explicit feature extraction. Our experimental results show\nthat traditional recurrent networks have inferior performance compared to tree\nrecurrent networks for the supervised PPI problem.\n
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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