SherLoc2: A High-Accuracy Hybrid Method for Predicting Subcellular Localization of Proteins
Bibliographic record
Abstract
SherLoc2 is a comprehensive high-accuracy subcellular localization prediction system. It is applicable to animal, fungal, and plant proteins and covers all main eukaryotic subcellular locations. SherLoc2 integrates several sequence-based features as well as text-based features. In addition, we incorporate phylogenetic profiles and Gene Ontology (GO) terms derived from the protein sequence to considerably improve the prediction performance. SherLoc2 achieves an overall classification accuracy of up to 93% in 5-fold cross-validation. A novel feature, DiaLoc, allows users to manually provide their current background knowledge by describing a protein in a short abstract which is then used to improve the prediction. SherLoc2 is available both as a free Web service and as a stand-alone version at http://www-bs.informatik.uni-tuebingen.de/Services/SherLoc2.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".