Generalized and High-throughput ¹³C Metabolic Flux Ratio Analysis by Machine Learning
dc.contributor.author
Zimmermann-Kogadeeva, Maria
dc.contributor.supervisor
Sauer, Uwe
dc.contributor.supervisor
Zamboni, Nicola
dc.date.accessioned
2017-06-14T07:36:22Z
dc.date.available
2017-06-14T07:36:22Z
dc.date.issued
2016
dc.identifier.uri
http://hdl.handle.net/20.500.11850/156109
dc.identifier.doi
10.3929/ethz-a-010805314
dc.format
application/pdf
dc.language.iso
en
dc.publisher
ETH Zürich
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
ZELLMETABOLISMUS + ZELLERNÄHRUNG (CYTOLOGIE)
dc.subject
ENZYMATISCHE ASPEKTE DES METABOLISMUS (BIOCHEMIE)
dc.subject
NICHT-RADIOAKTIVE MARKIERUNG + MARKIERUNG MIT STABILEN ISOTOPEN (BIOLOGISCHE TECHNIKEN)
dc.subject
MASCHINELLES LERNEN (KÜNSTLICHE INTELLIGENZ)
dc.subject
CELL NUTRITION + CELL METABOLISM (CYTOLOGY)
dc.subject
ENZYMATIC ASPECTS OF METABOLISM (BIOCHEMISTRY)
dc.subject
NON-RADIOACTIVE LABELLING + STABLE ISOTOPE LABELLING (BIOLOGICAL TECHNIQUES)
dc.subject
MACHINE LEARNING (ARTIFICIAL INTELLIGENCE)
dc.title
Generalized and High-throughput ¹³C Metabolic Flux Ratio Analysis by Machine Learning
dc.type
Doctoral Thesis
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2016
ethz.size
1 Band
ethz.code.ddc
DDC - DDC::5 - Science::570 - Life sciences
ethz.notes
Dissertation. ETH Zürich. 2016. No. 23931.
ethz.identifier.diss
23931
ethz.identifier.nebis
010805314
ethz.publication.place
Zürich
ethz.publication.status
published
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02030 - Dep. Biologie / Dep. of Biology
ethz.date.deposited
2017-06-14T07:38:21Z
ethz.source
ECOL
ethz.identifier.importid
imp59366ba617c6f25054
ethz.ecolpid
eth:50242
ethz.eth
yes
ethz.availability
Open access
ethz.rosetta.installDate
2017-07-31T15:47:20Z
ethz.rosetta.lastUpdated
2022-03-28T16:28:51Z
ethz.rosetta.versionExported
true
ethz.COinS
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Doctoral Thesis [29820]