NGBO: Introducing -omics metadata to biobanking ontology
Bibliographic record
Abstract
ABSTRACT Background With improvements in high throughput sequencing technologies and the constant generation of large biomedical datasets, biobanks increasingly take on the role of managing and delivering not just specimens but also data. However, re-using data from different biobanks is challenged by incompatible data representations. Contextual data describing biobank digital resources often contain unstructured textual information incompatible with computational processes such as automated data discovery and integration. Therefore, a consistent and comprehensive contextual data framework is needed to increase discovery, reusability, and integrability across data sources. Methods Based on available genomics standards (e.g., Minimum information about a microarray experiment (MIAME)), the College of American Pathologists (CAP) laboratory accreditation requirements, and the Open Biological and Biomedical Ontologies Foundry principles, we developed the Next Generation Biobanking Ontology (NGBO). In addition, we created new terms and re-used concepts from the Ontology for Biomedical Investigations (OBI) and the Ontology for Biobanking (OBIB) to build NGBO. Results The Next Generation Biobanking Ontology https://www.ebi.ac.uk/ols4/ontologies/ngbo is an open application ontology representing omics contextual data, licensed under the Apache License 2.0. The ontology focuses on capturing information about three main activities: wet bench analysis used to generate omics data, bioinformatics analysis used to process and interpret data, and data management. In this paper, we demonstrated the use of the NGBO to add semantic statements to real-life use cases and query data previously stored in unstructured textual format.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".