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Opportunities and Challenges in Democratizing Immunology Datasets.
Bhattacharya, Sanchita; Hu, Zicheng; Butte, Atul J.
  • Bhattacharya S; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.
  • Hu Z; Department of Pediatrics, University of California, San Francisco, San Francisco, CA, United States.
  • Butte AJ; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, United States.
Front Immunol ; 12: 647536, 2021.
Article in English | MEDLINE | ID: covidwho-1264331
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computational Biology / Allergy and Immunology / Datasets as Topic / Machine Learning / Immune System Type of study: Prognostic study / Randomized controlled trials / Reviews Limits: Humans Language: English Journal: Front Immunol Year: 2021 Document Type: Article Affiliation country: Fimmu.2021.647536

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Computational Biology / Allergy and Immunology / Datasets as Topic / Machine Learning / Immune System Type of study: Prognostic study / Randomized controlled trials / Reviews Limits: Humans Language: English Journal: Front Immunol Year: 2021 Document Type: Article Affiliation country: Fimmu.2021.647536