Exploring PSI-MI XML Collections Using DescribeX
Bibliographic record
Abstract
Summary PSI-MI has been endorsed by the protein informatics community as a standard XML data exchange format for protein-protein interaction datasets. While many public databases support the standard, there is a degree of heterogeneity in the way the proposed XML schema is interpreted and instantiated by different data providers. Analysis of schema instantiation in large collections of XML data is a challenging task that is unsupported by existing tools. In this study we use DescribeX, a novel visualization technique of (semi-)structured XML formats, to quantitatively and qualitatively analyze PSI-MI XML collections at the instance level with the goal of gaining insights about schema usage and to study specific questions such as: adequacy of controlled vocabularies, detection of common instance patterns, and evolution of different data collections. Our analysis shows DescribeX enhances understanding the instance-level structure of PSI-MI data sources and is a useful tool for standards designers, software developers, and PSI-MI data providers.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".