通过数据联合推进暴露组学的路线图

Exposome Pub Date : 2023-11-14 DOI:10.1093/exposome/osad010
Charles P Schmitt, Jeanette A Stingone, Arcot Rajasekar, Yuxia Cui, Xiuxia Du, Chris Duncan, Michelle Heacock, Hui Hu, Juan R Gonzalez, Paul D Juarez, Alex I Smirnov
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引用次数: 0

摘要

人类暴露体的规模涵盖了从怀孕到死亡所遇到的所有环境暴露,在管理、共享和整合无数相关数据类型和可用数据集以造福暴露体研究和公共卫生方面提出了重大挑战。通过解决这些挑战,暴露组学研究界将能够极大地扩展其收集新发现研究数据的能力,构建和更新新的暴露组学数据集,用于构建基于人工智能和机器学习的模型,快速调查新出现的问题,并推进数据驱动科学的应用。该领域的多样性跨越了科学学科的多个子领域和不同的环境背景,因此需要采用数据联合方法在众多地理上和管理上分离的数据资源之间架起桥梁,这些数据资源具有不同的使用、隐私、访问、分析和可发现性能力和约束。本文提出了用例、挑战、机遇和建议,供暴露学社区建立和成熟联合暴露学数据生态系统。
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A roadmap to advance exposomics through federation of data
Abstract The scale of the human exposome, which covers all environmental exposures encountered from conception to death, presents major challenges in managing, sharing, and integrating a myriad of relevant data types and available data sets for the benefit of exposomics research and public health. By addressing these challenges, the exposomics research community will be able to greatly expand on its ability to aggregate study data for new discoveries, construct and update novel exposomics data sets for building artificial intelligence and machine learning-based models, rapidly survey emerging issues, and advance the application of data-driven science. The diversity of the field, which spans multiple subfields of science disciplines and different environmental contexts, necessitates adopting data federation approaches to bridge between numerous geographically and administratively separated data resources that have varying usage, privacy, access, analysis, and discoverability capabilities and constraints. This paper presents use cases, challenges, opportunities, and recommendations for the exposomics community to establish and mature a federated exposomics data ecosystem.
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Exposomics: perfection not required. A long and winding road: Culture change on data sharing in exposomics Interactive data sharing for multiple questionnaire-based exposome-wide association studies and exposome correlations in the Personalized Environment and Genes Study. Questionnaire-based exposome-wide association studies for common diseases in the Personalized Environment and Genes Study. Decoding the exposome: Data science methodologies and implications in Exposome-Wide association studies (ExWASs)
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