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免疫数据集民主化的机遇与挑战。

Opportunities and Challenges in Democratizing Immunology Datasets.

机构信息

Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.

Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.

出版信息

Front Immunol. 2021 Apr 16;12:647536. doi: 10.3389/fimmu.2021.647536. eCollection 2021.

Abstract

The field of immunology is rapidly progressing toward a systems-level understanding of immunity to tackle complex infectious diseases, autoimmune conditions, cancer, and beyond. In the last couple of decades, advancements in data acquisition techniques have presented opportunities to explore untapped areas of immunological research. Broad initiatives are launched to disseminate the datasets siloed in the global, federated, or private repositories, facilitating interoperability across various research domains. Concurrently, the application of computational methods, such as network analysis, meta-analysis, and machine learning have propelled the field forward by providing insight into salient features that influence the immunological response, which was otherwise left unexplored. Here, we review the opportunities and challenges in democratizing datasets, repositories, and community-wide knowledge sharing tools. We present use cases for repurposing open-access immunology datasets with advanced machine learning applications and more.

摘要

免疫学领域正在朝着系统水平理解免疫的方向快速发展,以应对复杂的传染病、自身免疫性疾病、癌症等。在过去的几十年中,数据采集技术的进步为探索免疫学研究的未开发领域提供了机会。广泛的倡议旨在传播分散在全球、联邦或私人存储库中的数据集,促进不同研究领域的互操作性。同时,计算方法的应用,如网络分析、荟萃分析和机器学习,通过提供对影响免疫反应的显著特征的深入了解,推动了该领域的发展,否则这些特征将无法得到探索。在这里,我们回顾了使数据集、存储库和全社区知识共享工具民主化的机会和挑战。我们提出了使用高级机器学习应用程序和更多其他工具重新利用开放获取免疫学数据集的用例。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8ee5/8086961/7a5498bf0141/fimmu-12-647536-g001.jpg

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