Bibliographic record
Abstract
In a previous work, we built a classifier that used a decision tree to predict fungal protein localization based on physiochemical properties of proteins. 178 features selected from proteins compositional properties, functional motifs and signal sequences were studied for their effect on subcellular localization. That work resulted in a localizer that would successfully predict some of the reported localizations in 64% of the cases and all the reported localizations in 49% of the cases. Here, we improve on the results of the mentioned work by streamlining the classes of protein features used. Considering various modes of intra-cellular protein movement and the requirements for such transport, we establish a list of features that would have direct impact on the recognition of the proteins by the transport machinery of the cell. We shall detect the occurrence of such features in fungal proteins and use them as potential determinants of subcellular localization. The system rebuilt based on 980 of such features is validated using a 5-fold cross validation and results in a success rate of 87% for predicting some and 77% for predicting all the reported localization sites of 3 fungal species for which annotations on subcellular localization were available.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".