Preprocessing homologous regions in annotated protein sequences concerning machine-learning applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Accurate preprocessing of annotated protein sequences with regard to homologies is essential for maintaining the integrity of machine-learning applications. This study presents two new tools—HAM (Homology-based Annotation Masking) and HAC (Homology Annotation Conflict)— designed to address these challenges. HAM detects and masks homologous regions between datasets to prevent leakage, while HAC identifies and resolves annotation inconsistencies within datasets. Applying these tools to three benchmark datasets revealed substantial overlooked homology and annotation conflicts, even in datasets that had been previously clustered by sequence identity. These findings underscore the importance of homology-aware preprocessing to ensure the integrity of model training and evaluation. By integrating HAM and HAC into machine learning workflows, researchers can improve the consistency and trustworthiness of protein sequence-based predictions. Availability github.com/NawarMalhis/HAM.git
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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