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. 2018 Sep 26;19(1):341.
doi: 10.1186/s12859-018-2374-0.

Prototyping a precision oncology 3.0 rapid learning platform

Connor Sweetnam  1 , Simone Mocellin  2 , Michael Krauthammer  3   4 , Nathaniel Knopf  1   5 , Robert Baertsch  1 , Jeff Shrager  6   7
Affiliations

Prototyping a precision oncology 3.0 rapid learning platform

Connor Sweetnam et al. BMC Bioinformatics. .

Abstract

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.

Keywords: Controlled natural language; Nanopublication; Natural language processing; Precision oncology; Rapid learning; Targeted therapies; Treatment reasoning; Tumor boards.

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Conflict of interest statement

Ethics approval and consent to participate

n/a

Consent for publication

n/a

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
The envisioned rapid learning loop, supported by our prototype platform
Fig. 2
Fig. 2
TrEx -- The Treatment Explorer. The user selects from among possible observations in the lower table, and the Mocellin algorithm ranks treatment hypotheses in the top table
Fig. 3
Fig. 3
TRs that participate in ranking PLX-4032
Fig. 4
Fig. 4
TRs related to BRAF V600E mutation
Fig. 5
Fig. 5
The Quark Analyst’s Dashboard
Fig. 6
Fig. 6
the TR Vetting tool
Fig. 7
Fig. 7
Case context for the EIF2AK3-ALK fusion TR
Fig. 8
Fig. 8
TrEx with the vetted EIF2AK3-ALK fusion TR
Fig. 9
Fig. 9
The (prototype) “Insights in Oncology” Nano-Journal
See this image and copyright information in PMC

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