BRIDGE Center Standards Core

BRIDGE中心标准核心项目总结

BRIDGE Center Standards Core Project Summary AI offers great potential for the discovery of novel biomedical insights from linkages between disparate, cross-domain datasets. Unfortunately, traditional hypothesis-driven datasets tend to be narrowly focused on the targeted problem domain with little consideration to “AI-readiness”. To best enable the use of such datasets in data-driven and cross-domain discovery, they must be made Findable, Accessible, Interoperable, and Reusable (FAIR). Lack of FAIRness is particularly problematic for AI, which is data-hungry. To fully leverage the power of AI approaches, researchers need to find and reuse data to combine into larger datasets, and the data must be interoperable or harmonized to be combined meaningfully. Transforming pre-existing datasets into AI-ready data is challenging, requiring extensive linking and curation by human experts. This challenge is exacerbated when annotating and linking data across domains, where standards may be disparate in purpose and specificity. Finally, many datasets do not adhere to best practices in data transparency, including content attribution and conditions on distribution and reuse. These additional considerations of Traceability, Licensing, and Connectedness create an operationalized model for FAIR: FAIR-TLC. Overcoming the barriers to FAIR-TLC is key to translational science and AI-driven biomedical discovery. Our team has led standards development efforts in numerous large consortia, including the GA4GH, HL7, and N3C. Our standards for representing biomedical concepts have been widely adopted, including those for human phenotypes (e.g., HPO, GA4GH Phenopackets), diseases (NCIt, Mondo, ICD-11), genes (Gene Ontology), anatomy (Uberon), and molecular variation (GA4GH VRS). We have developed standards and tools to address data provenance (SEPIO), contributions (Contributor Attribution Model), licensing barriers (Data Use Ontology, Reusable Data Project), and connectivity (Linked data Model Language, LinkML). We will build on our previous work, collaborative skills, and technical knowledge to develop a framework to enable the harmonization of standards across biomedical domains. We will form working groups with representatives of the Data Generation Projects (DGPs) to document use cases and synthesize data standard requirements. We will provide protocols and training for specifying standards, and provide concierge services in support of all deliverables and activities. We will create a version-controlled Bridge2AI Standards Registry to inventory standards for use by the DGPs, specified in the modality-agnostic LinkML framework, discoverable through the interactive Standards Hub, and automatically exportable to technical artifacts through our Data Transformation Toolbox. We will build a Standards Evaluation Dashboard for assessment and discovery of standards in datasets from Bridge2AI Data Generation Projects. We will promote best practices in the transparent and responsible sharing of datasets and ML models through DUO, Datasheets, and Model Cards.