AstroID resource: a scalable, relational database structure for longitudinal biomarker discovery
Bibliographic record
Abstract
BACKGROUND: The biological sciences are producing increasingly larger datasets for biomarker discovery. While common data models have been developed for medical terms as they relate to patient health outcomes, a data model that supports longitudinal tracking of biospecimens and relating them against an individual patient experience is a large, unmet need. METHOD: A structure and associated taxonomy were achieved through a six-tier build in Research Electronic Data CAPture (REDCap), which organizes the complexity of the therapeutic decisions, biospecimens, and outcomes that characterize a longitudinal patient experience. Modules were developed to support export of REDCap data into a Structured Query Language (SQL) format for merging with extended biomarker data, also housed in SQL. RESULTS: The resultant AstroID resource is a relational structure for clinical and biospecimen data that meets several desired goals: searchable, flexible, generic, Health Insurance Portability and Accountability Act-compliant, auditable, and easy-to-use. The essential elements forming the core of the six-tiered build are provided, so others can readily adopt this schema, as well as an example of an extended, customized build to support biomarker discovery for patients with melanoma. Two examples where this data structure was used to support biomarker discovery and development are described, and example queries of the database are also presented. To the extent possible, the data dictionary was aligned with large data models, such as those for the National Institutes of Health's Human Tumor Atlas Network. The structure can readily scale to accommodate thousands of patients, multimodality data, and spatial characterization of billions of cells. Radiologic imagery can also be included along with pathology imagery to support spatial studies, including artificial intelligence-driven analyses. CONCLUSIONS: This effort provides a database model for investigators conducting research on large volumes of biospecimens with clinical annotation. We have now deployed this structure in our laboratories and have over 1B cells spatially mapped, each effectively tagged with the clinical information from longitudinal patient experiences. While the description uses the example of cancer biomarkers, this data structure could be used to characterize longitudinal biospecimens from any disease process. In the near future, automatic synchronization between the electronic medical record and one or more AstroID databases is anticipated.
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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 | 0.015 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".