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ZENODO
Conference object . 2017
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Conference object . 2017
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2017
License: CC BY
Data sources: ZENODO
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Googling The Cancer Genome

descriptionPublicationkeyboard_double_arrow_right Conference object , Other literature type 01 Nov 2017 English Publisher:Zenodo
Authors: Carl Shneider; Jeroen de Ridder; Wigard Kloosterman; Marleen Nieboer; Sonja Georgievska; Arnold Kuzniar;

doi: 10.5281/zenodo.1040163 , 10.5281/zenodo.1040164

Googling The Cancer Genome

Abstract

Associated Links: https://www.esciencecenter.nl/project/googling-the-cancer-genome https://blog.esciencecenter.nl/teaching-machines-to-recognize-cancer-420da40f547d

Poster presentation at the National eScience Symposium 2017, The Netherlands.

Related Organizations
Keywords

Deep Learning, Whole-Genome Sequencing (WGS), Structural Variants, Cancer

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
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