Probabilistic Annotations of Protein Sequences for Intrinsically Disordered Features
Bibliographic record
Abstract
Abstract This paper introduces a novel platform for IDR Probabilistic Annotation (IPA). The IPA platform now encompasses tools for predicting ‘Linker’ regions and ‘nucleic’, ‘protein’, and ‘all’ (protein or nucleic) IDR binding sites within protein amino acid sequences. Despite its simplicity and computational efficiency, results demonstrate that IPA performs competitively with leading tools in predicting ‘protein’ and ‘all’ IDR binding sites while considerably outperforming all tools in identifying Linker regions and nucleic binding sites. An important contribution of this work is the introduction of a new output paradigm for computational feature predictions. Traditional tools typically express predictions as scores, with higher values indicating greater probabilities. However, these scores lack true probabilistic meaning and interpretability, even derived from logistic regression models. This limitation arises primarily because training data priors differ from broader populations’ unknown priors. This paper proposes applying a reverse Bayes Rule to logistic regression outputs, effectively normalizing for the priors in the training data. This adjustment produces scores representing actual probabilities, assuming 50% priors in the general population. Such scores are interpretable in isolation and enable comparability and integration across different tools, marking a significant step toward standardization in feature prediction methodologies. Availability orca.msl.ubc.ca/nmshare/ipa.tar.gz
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".