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From space to biomedicine: Enabling biomarker data science in the cloud

Crichton, D. J. and Cinquini, L. and Kincaid, H. and Mahabal, A. and Altinok, A. and Anton, K. and Colbert, M. and Kelly, S. and Liu, D. and Patriotis, C. and Lombeyda, S. and Srivastava, S. (2022) From space to biomedicine: Enabling biomarker data science in the cloud. Cancer Biomarkers, 33 (4). pp. 479-488. ISSN 1875-8592. doi:10.3233/cbm-210350. https://resolver.caltech.edu/CaltechAUTHORS:20220624-845847800

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Abstract

NASA’s Jet Propulsion Laboratory (JPL) is advancing research capabilities for data science with two of the National Cancer Institute’s major research programs, the Early Detection Research Network (EDRN) and the Molecular and Cellular Characterization of Screen-Detected Lesions (MCL), by enabling data-driven discovery for cancer biomarker research. The research team pioneered a national data science ecosystem for cancer biomarker research to capture, process, manage, share, and analyze data across multiple research centers. By collaborating on software and data-driven methods developed for space and earth science research, the biomarker research community is heavily leveraging similar capabilities to support the data and computational demands to analyze research data. This includes linking diverse data from clinical phenotypes to imaging to genomics. The data science infrastructure captures and links data from over 1600 annotations of cancer biomarkers to terabytes of analysis results on the cloud in a biomarker data commons known as “LabCAS”. As the data increases in size, it is critical that automated approaches be developed to “plug” laboratories and instruments into a data science infrastructure to systematically capture and analyze data directly. This includes the application of artificial intelligence and machine learning to automate annotation and scale science analysis.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3233/cbm-210350DOIArticle
ORCID:
AuthorORCID
Mahabal, A.0000-0003-2242-0244
Additional Information:© 2022 IOS Press. Received 6 July 2021; Accepted 11 September 2021; Published: 18 April 2022. The research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004).
Funders:
Funding AgencyGrant Number
NASA/JPL/Caltech80NM0018D0004
Subject Keywords:Data science, data analysis, big data, cloud computing, machine learning, artificial intelligence
Issue or Number:4
DOI:10.3233/cbm-210350
Record Number:CaltechAUTHORS:20220624-845847800
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220624-845847800
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115265
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:24 Jun 2022 23:21
Last Modified:28 Jun 2022 19:24

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