BRIDGE Center Standards Core

BRIDGE 中心标准核心

• 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
• 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

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.

桥梁中心标准核心项目摘要 AI为发现新颖的生物医学见解提供了巨大的潜力 跨域数据集。不幸的是，传统假设驱动的数据集倾向于狭窄地关注 有针对性的问题域几乎没有考虑“ AI RERVERINSE”。为了最好地启用此类数据集 在数据驱动和跨域的发现中，必须使它们可以找到，可访问，可互操作，并且 可重复使用（公平）。缺乏公平性对于渴望数据的AI尤其有问题。充分利用 AI方法的功能，研究人员需要查找和重复使用数据以将数据合并到较大的数据集中，并且数据 必须互操作或协调一致才能有意义地组合。将预先存在的数据集转换为 AI-Ready数据是挑战，需要人类专家的广泛联系和策划。这个挑战是 在注释和链接跨域的数据时会加剧，其中标准可能在目的上是不同的 和特异性。最后，许多数据集不遵守数据透明度的最佳实践，包括内容 分配和重复使用的归因和条件。可追溯性，许可， 连接性创建一个公平的操作模型：fair-tlc。 克服Fair-TLC的障碍是翻译科学和AI驱动的生物医学发现的关键。我们的 团队领导了许多大型财团的标准发展工作，包括GA4GH，HL7和 N3C。我们代表生物医学概念的标准已被广泛采用，包括 人类表型（例如HPO，GA4GH Phenopackets），疾病（NCIT，Mondo，ICD-11），基因（基因 本体论），解剖学（Uberon）和分子变异（GA4GH VRS）。我们已经开发了标准和工具 解决数据出处（SEPIO），贡献（贡献归因模型），许可障碍（数据 使用本体论，可重复使用的数据项目）和连接性（链接数据模型语言，linkML）。 我们将基于以前的工作，协作技能和技术知识来建立一个框架 启用跨生物医学领域的标准统一。我们将与 数据生成项目（DGP）的代表记录用例并合成数据标准 要求。我们将为指定标准提供协议和培训，并提供礼宾服务 支持所有可交付成果和活动。我们将创建一个由版本控制的Bridge2AI标准注册表 DGP使用的库存标准，在模态链接框架中指定，可发现的库存标准 通过交互式标准中心，并通过我们的数据自动出口到技术文物 转换工具箱。我们将建立标准评估仪表板，以评估和发现 Bridge2AI数据生成项目数据集中的标准。我们将促进最佳实践 通过二人组，数据表和模型卡透明和负责共享数据集和ML模型。