TBIO-27. GABRIELLA MILLER KIDS FIRST DATA RESOURCE CENTER ADVANCING GENETIC RESEARCH IN CHILDHOOD CANCER AND STRUCTURAL BIRTH DEFECTS THROUGH LARGE SCALE INTEGRATED DATA-DRIVEN DISCOVERY AND CLOUD-BASED PLATFORMS FOR COLLABORATIVE ANALYSIS
Bibliographic record
Abstract
Childhood cancers and structural birth share a common context of context in developmental biology that remains poorly defined. Indeed, epidemiologically, pediatric central nervous system tumors are some of the most frequently co-occurring cancers in children with birth defects. While researchers have been increasingly identifying the underlying biological causes of these conditions, the role of genetics and/or shared pathways across cancer and birth defects is not yet fully understood. A better understanding of a common developmental setting could spur advancements in prevention, early detection, and therapeutic interventions that will improve the lives of the children and families impacted by these conditions. The Common Fund’s Gabriella Miller Kids First Pediatric Research Program represents a national initiative focused on developing a large-scale genomic data supported by a data resource center (DRC) that will empower collaborative discovery and shared resources for research. The DRC will allow researchers everywhere access to vast amounts of childhood cancer and structural birth defects through cloud-based computational and analytics portals including CAVATICA (cavatica.org). Approximately 8,000 patient samples will be ready for analysis at the launch this year including a data cohort of more than 2000 pediatric brain tumor WGS/RNAseq provided by the Children’s Brain Tumor Tissue Consortium (CBTTC) and the Pacific Pediatric Neuro-Oncology Consortium (PNOC). More than 25,000 WGS are expected to be processed by 2019, making the Kids First Data Resource Center the largest pediatric data cohort of its kind.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.139 | 0.108 |
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".