Xenbase: 25 years of integrating molecular and biomedical data from <i>Xenopus</i>
Bibliographic record
Abstract
The Xenopus model organism knowledgebase, Xenbase (www.xenbase.org), bridges a wide variety of data types including genomes, anatomy, phenotypes, proteins, diseases and more. The goal of Xenbase is to support Xenopus molecular, cell and developmental biology research, to make these data available to the broader biomedical ecosystem, and accelerate the translation of Xenopus research into knowledge that will improve human health. Connections are made between data through relationships in our core data model and via a series of ontologies that serve as graph-based maps that can be traversed in various dimensions to find connections within our vast corpus of data. Data is input by a team of expert curators applying FAIR data management principles and also via automated pipelines and data processing routines. While our main focus is embryonic development and cell biology, these are often the underlying causes of compromised human health and are therefore invaluable for exploring the medical impacts of DNA sequence variants identified through patient exome or whole genome sequencing. One of the foundational elements in Xenbase with our gene-centric data structure is genomes, and we have recently vastly improved the quality of these core resources for both Xenopus laevis and Xenopus tropicalis. These and an extensive suite of other improvements are described, including updates and upgrades in content types, software and systems.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.029 |
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".