The CFDE has funded collaborative DCC partnership projects that will develop approaches and tools to harmonize data and workflows from multiple Common Fund programs enabling cross-dataset analysis. These partnerships are meant to enhance DCC-DCC interactions. In addition, these partnerships aim to demonstrate the utility of their data integration tools and approaches for CF datasets to the broader scientific community.
The project aims to refine and populate the biomarker data model through a close and dynamic external partnership with the NCI-supported Early Detection Research Network (EDRN) with built-in community input mechanisms.
The project aims to develop a transformative cloud-based integrative visualization platform to enable interactive exploration of multimodal biomedical data, using large omics and imaging datasets sourced from CFDE DCCs and external consortia.
This project aims to significantly enhance our understanding of the genetic links between childhood cancers and structural birth defects, which are among the leading causes of pediatric morbidity and mortality.
The project aims to define the Human Metabotype, which is human metabolic status defined through genetic, diet, environmental exposure, and exercise perturbations.
The partnership aims to support functional genomics towards fundamental biological systems understanding and the development of new diagnostics and therapies. This includes supporting data integration, reusing, and building new capabilities for interrogating and understanding complex biological systems and cell networks.
This project developed methods to harmonize gene, protein, and variant identifiers across the CF DCCs by integrating DCC APIs and gene landing pages.
Publication:
10.1093/bioadv/vbac013
This project develops an interactive workflow engine that draws knowledge from across CF DCCs using their APIs.
This project developed a knowledge graph that connects genes, birth defects, and drugs to discover reproductive toxicity potential for preclinical compounds.
Publication:
10.1038/s43856-023-00329-2