Abstract 6576: Gabriella Miller Kids First Data Resource Center (KFDRC): Empowering discovery across germline and somatic variation in pediatric cancer
Bibliographic record
Abstract
Abstract The Gabriella Miller Kids First Pediatric Research Program (Kids First) has enabled genomic and transcriptomic characterization across a multitude of pediatric diseases at an unprecedented scale. This includes both pediatric cancer cohorts and structure birth defect cohorts, as co-occurrences of these suggest a shared context of developmental biology alterations. Making this quantity of data findable, accessible, interoperable and reusable (FAIR) to researchers around the world has been enabled via the Gabriella Miller Kids First Data Resource Center (KFDRC). Twenty-six Kids First studies are released on the Kids First Data Resource Portal, representing more than 21,000 participants and more than 1.25 PB of data, with additional datasets being released yearly. Additionally, via a partnership between NCI Childhood Cancer Data Initiative and Kids First, large studies currently undergoing sequencing on childhood sarcomas and brain tumors will also be made available via the KFDRC. The KFDRC Portal (https://portal.kidsfirstdrc.org/) provides an interactive cohort building interface as well as powerful search capabilities across 61 billion germline variants in real-time. The somatic variants are made available on the open-access PedcBioPortal (https://pedcbioportal.kidsfirstdrc.org/). Search features include leveraging ontologies to enable different granularity across studies to be cross-querable. The use of these ontologies enable semantic interoperability, while also leveraging FHIR as an interoperable standard for exchange of clinical and other relevant metadata. GA4GH DRS and Passport services via Gen3 and NIH’s Researcher Authentication Service (RAS) enable streamlined access to controlled access data within researchers’ cloud platform of choice, including CAVATICA (https://cavatica.org) which is directly integrated with the KFDRC Portal. The KFDRC is part of the NIH Cloud-Based Platform Interoperability efforts, which includes the NCI Cancer Research Data Commons. This enables discovery and analysis across combined cancer datasets and data modalities and further potential for the cross analysis between cancers and structural birth defects to accelerate the discovery process to ultimately lead to improved knowledge and outcomes for childhood cancer and more generally pediatric disease from genetic causes. Citation Format: David Higgins, Jean-Philippe Thibert, Michele Mattioni, Jack DiGiovanna, Robert L. Grossman, Bailey K. Farrow, Eric Wenger, Samuel Volchenboum, Robert J. Carroll, Melissa A. Haendel, Deanne M. Taylor, Yuankun Zhu, Vincent Ferretti, Adam C. Resnick, Allison P. Heath. Gabriella Miller Kids First Data Resource Center (KFDRC): Empowering discovery across germline and somatic variation in pediatric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6576.
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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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.151 | 0.087 |
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".