An Experiment on Using Temporal Ontologies to Reason about Localization and Transport of Fungal Proteins
Bibliographic record
Abstract
The same protein has been observed, through methods of direct assay, to localize to various compartments of the cell. Finding the order in which such subcellular localizations take place contributes toward elucidation of protein pathways and protein-protein interactions. On the other hand, incorporation of the hierarchy of cellular compartments organized as a tree allows a more clear reasoning in terms of the paths taken by proteins that localize to multiple overlapping subcellular sites. In this work, we build an ontology to serve as a knowledge repository for localization of fungal proteins to a hierarchy of major subcellular sites and the order in which such localizations take place. We use this ontology to automatically classify fungal proteins as per their localizations or according to their speciflc characteristics. Finally, we develop a menu-driven user interface to interact with the constructed ontology. Based on a template of application scenarios, user selections are translated into executable queries to be posed to the system.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".