Prototyping a precision oncology 3.0 rapid learning platform

BACKGROUND: We describe a prototype implementation of a platform that could underlie a Precision Oncology Rapid Learning system. RESULTS: We describe the prototype platform, and examine some important issues and details. In the Appendix we provide a complete walk-through of the prototype platform. C...

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Autores principales: Sweetnam, Connor, Mocellin, Simone, Krauthammer, Michael, Knopf, Nathaniel, Baertsch, Robert, Shrager, Jeff
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158802/
https://www.ncbi.nlm.nih.gov/pubmed/30257653
http://dx.doi.org/10.1186/s12859-018-2374-0
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author Sweetnam, Connor
Mocellin, Simone
Krauthammer, Michael
Knopf, Nathaniel
Baertsch, Robert
Shrager, Jeff
author_facet Sweetnam, Connor
Mocellin, Simone
Krauthammer, Michael
Knopf, Nathaniel
Baertsch, Robert
Shrager, Jeff
author_sort Sweetnam, Connor
collection PubMed
description BACKGROUND: We describe a prototype implementation of a platform that could underlie a Precision Oncology Rapid Learning system. RESULTS: We describe the prototype platform, and examine some important issues and details. In the Appendix we provide a complete walk-through of the prototype platform. CONCLUSIONS: The design choices made in this implementation rest upon ten constitutive hypotheses, which, taken together, define a particular view of how a rapid learning medical platform might be defined, organized, and implemented.
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spelling pubmed-61588022018-10-01 Prototyping a precision oncology 3.0 rapid learning platform Sweetnam, Connor Mocellin, Simone Krauthammer, Michael Knopf, Nathaniel Baertsch, Robert Shrager, Jeff BMC Bioinformatics Software BACKGROUND: We describe a prototype implementation of a platform that could underlie a Precision Oncology Rapid Learning system. RESULTS: We describe the prototype platform, and examine some important issues and details. In the Appendix we provide a complete walk-through of the prototype platform. CONCLUSIONS: The design choices made in this implementation rest upon ten constitutive hypotheses, which, taken together, define a particular view of how a rapid learning medical platform might be defined, organized, and implemented. BioMed Central 2018-09-26 /pmc/articles/PMC6158802/ /pubmed/30257653 http://dx.doi.org/10.1186/s12859-018-2374-0 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Software
Sweetnam, Connor
Mocellin, Simone
Krauthammer, Michael
Knopf, Nathaniel
Baertsch, Robert
Shrager, Jeff
Prototyping a precision oncology 3.0 rapid learning platform
title Prototyping a precision oncology 3.0 rapid learning platform
title_full Prototyping a precision oncology 3.0 rapid learning platform
title_fullStr Prototyping a precision oncology 3.0 rapid learning platform
title_full_unstemmed Prototyping a precision oncology 3.0 rapid learning platform
title_short Prototyping a precision oncology 3.0 rapid learning platform
title_sort prototyping a precision oncology 3.0 rapid learning platform
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158802/
https://www.ncbi.nlm.nih.gov/pubmed/30257653
http://dx.doi.org/10.1186/s12859-018-2374-0
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