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1
Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data
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Machine learning workflows to estimate class probabilities for precision cancer diagnostics on DNA methylation microarray data

Nature protocols, 2020-02, Vol.15 (2), p.479-512 [Peer Reviewed Journal]

COPYRIGHT 2020 Nature Publishing Group ;COPYRIGHT 2020 Nature Publishing Group ;2020© The Author(s), under exclusive licence to Springer Nature Limited 2020 ;The Author(s), under exclusive licence to Springer Nature Limited 2020. ;info:eu-repo/semantics/openAccess ;ISSN: 1754-2189 ;EISSN: 1750-2799 ;DOI: 10.1038/s41596-019-0251-6 ;PMID: 31932775

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2
Automatic Classification of Auroral Images From the Oslo Auroral THEMIS (OATH) Data Set Using Machine Learning
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Article
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Automatic Classification of Auroral Images From the Oslo Auroral THEMIS (OATH) Data Set Using Machine Learning

Journal of geophysical research. Space physics, 2018-07 [Peer Reviewed Journal]

info:eu-repo/semantics/openAccess ;ISSN: 2169-9380 ;EISSN: 2169-9402 ;DOI: 10.1029/2018JA025274

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3
Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia
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Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia

PloS one, 2019-05, Vol.14 (5), p.e0216756-e0216756 [Peer Reviewed Journal]

COPYRIGHT 2019 Public Library of Science ;COPYRIGHT 2019 Public Library of Science ;2019 Picon et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;info:eu-repo/semantics/openAccess ;2019 Picon et al 2019 Picon et al ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0216756 ;PMID: 31107876

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4
Coming up short: Identifying substrate and geographic biases in fungal sequence databases
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Coming up short: Identifying substrate and geographic biases in fungal sequence databases

Fungal ecology, 2018-12 [Peer Reviewed Journal]

info:eu-repo/semantics/openAccess ;ISSN: 1754-5048 ;DOI: 10.1016/j.funeco.2018.08.002

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