Ontology-based transporter substrate annotation for benchmark datasets
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
Construction of benchmark datasets for supervised learning requires a label or class to be assigned to each datapoint. This is done by the constructor of the dataset in those cases where the label is not directly taken from a reference source. In transporter substrate prediction, during the dataset construction step, a class is assigned to each protein that reflects the substrate transported across the biological membrane. This substrate class assignment is typically conducted through manual curation process in which details regarding the assignment are not explained. Biological databases are consistently growing and many entries are updated; therefore, automating the data collection stage is desirable. This work aims to automate the transporter substrate data collection process in a consistent and reproducible manner, and eliminate external dataset curator judgment. To achieve this, we propose an automated tool that assigns a substrate class by using available annotations and delegating the broader class assignment to previously established ontologies. Two case studies have been used to evaluate the automation tool and to analyze the available number of substrates in the current biological databases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it