BRIDGE Center Standards Core

BRIDGE 中心标准核心

基本信息

批准号：
10661029
负责人：
Monica Cecilia Munoz-Torres
金额：
$ 133.76万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-06 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10661029
关键词：
Address Adopted Adoption Anatomy Artificial Intelligence Awareness Back Behavioral Benchmarking Bridge to Artificial Intelligence Businesses Communities Confusion Consultations Consumption Data Data Collection Data Engineering Data Provenance Data Scientist Data Set Deposition Development Discipline Disease Disparate Documentation Ecosystem Elements Ensure Environment Equipment and supply inventories Evaluation FAIR principles Generations Genes Goals Human Investments Knowledge Language Licensing Life Cycle Stages Link Machine Learning Modality Modeling Modernization Molecular Morphologic artifacts Ontology Output Phenotype Protocols documentation Provider Quality Control Readiness Registries Reporting Reproducibility Research Research Personnel Resources Sea Semantics Services Source Specific qualifier value Specificity Standardization System Terminology Time Training Translational Research United States National Institutes of Health Update Variant Vocabulary Work dashboard data dissemination data harmonization data ingestion data modeling data quality data reuse data sharing data standards empowerment insight interoperability large datasets machine learning model novel open source programs quality assurance response skills tool web portal working group

项目摘要

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.

BRIDGE 中心标准核心项目摘要人工智能为从不同的、不幸的是，传统的假设驱动数据集往往只关注跨领域数据集。很少考虑“人工智能就绪性”的目标问题领域，以便最好地使用此类数据集。在数据驱动和跨领域发现中，它们必须变得可查找、可访问、可互操作和可重复使用（公平）对于需要充分利用数据的人工智能来说，缺乏公平性尤其成问题。借助人工智能方法的力量，研究人员需要查找并重用数据以组合成更大的数据集，并且这些数据必须可互操作或协调才能将现有数据集有意义地组合起来。人工智能就绪的数据具有挑战性，需要人类专家进行广泛的链接和管理。在跨域注释和链接数据时会加剧，其中标准的目的可能不同最后，许多数据集不遵守数据透明度的最佳实践，包括内容。分配和再利用的归属和条件。可追溯性、许可的这些额外考虑因素。和连通性为 FAIR 创建了一个可操作的模型：FAIR-TLC。克服 FAIR-TLC 障碍是转化科学和人工智能驱动的生物医学发现的关键。该团队领导了众多大型联盟的标准开发工作，包括 GA4GH、HL7 和我们代表生物医学概念的标准已被广泛采用，包括那些人类表型（例如 HPO、GA4GH Phenopackets）、疾病（NCIt、Mondo、ICD-11）、基因（Gene 我们开发了标准和工具。解决数据来源 (SEPIO)、贡献（贡献者归因模型）、许可障碍（数据使用本体、可重用数据项目）和连接性（链接数据模型语言，LinkML）。我们将在之前的工作、协作技能和技术知识的基础上开发一个框架我们将与各生物医学领域建立工作组。数据生成项目 (DGP) 的代表记录用例并综合数据标准我们将提供指定标准的协议和培训，并提供礼宾服务。为了支持所有可交付成果和活动，我们将创建一个版本控制的 Bridge2AI 标准注册表。 DGP 使用的清单标准，在与模态无关的 LinkML 框架中指定，可发现通过交互式标准中心，并通过我们的数据自动导出到技术工件我们将构建一个标准评估仪表板，用于评估和发现 Bridge2AI 数据生成项目的数据集标准我们将推广最佳实践。通过 DUO、数据表和模型卡透明、负责任地共享数据集和 ML 模型。