Systematic tissue annotations of genomics samples by modeling unstructured metadata

Nathaniel T. Hawkins; Marc Maldaver; Anna Yannakopoulos; Lindsay A. Guare; Arjun Krishnan

doi:10.1038/s41467-022-34435-x

Nature Communications (Nov 2022)

Systematic tissue annotations of genomics samples by modeling unstructured metadata

Nathaniel T. Hawkins,
Marc Maldaver,
Anna Yannakopoulos,
Lindsay A. Guare,
Arjun Krishnan

Affiliations

Nathaniel T. Hawkins: Department of Computational Mathematics, Science and Engineering, Michigan State University
Marc Maldaver: Department of Computational Mathematics, Science and Engineering, Michigan State University
Anna Yannakopoulos: Department of Computational Mathematics, Science and Engineering, Michigan State University
Lindsay A. Guare: Department of Computational Mathematics, Science and Engineering, Michigan State University
Arjun Krishnan: Department of Computational Mathematics, Science and Engineering, Michigan State University

DOI: https://doi.org/10.1038/s41467-022-34435-x
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 13

Abstract

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The 1+ million publicly-available human –omics samples currently remain acutely underused. Here the authors present an approach combining natural language processing and machine learning to infer the source tissue of public genomics samples based on their plain text descriptions, making these samples easy to discover and reuse.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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