Computational methods for dissecting cancer heterogeneity using multi-modal data: from bulk to spatial resolution
dc.contributor.author
Ficek-Pascual, Joanna
dc.contributor.supervisor
Rätsch, Gunnar
dc.contributor.supervisor
Koelzer, Viktor H.
dc.contributor.supervisor
Lehmann, Kjong-Van
dc.contributor.supervisor
Conesa, Ana
dc.date.accessioned
2023-11-30T12:02:17Z
dc.date.available
2023-11-29T21:57:14Z
dc.date.available
2023-11-30T12:02:17Z
dc.date.issued
2023
dc.identifier.uri
http://hdl.handle.net/20.500.11850/644527
dc.identifier.doi
10.3929/ethz-b-000644527
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
ETH Zurich
en_US
dc.title
Computational methods for dissecting cancer heterogeneity using multi-modal data: from bulk to spatial resolution
en_US
dc.type
Doctoral Thesis
dc.date.published
2023-11-30
ethz.size
288 p.
en_US
ethz.code.ddc
DDC - DDC::5 - Science::570 - Life sciences
en_US
ethz.code.ddc
DDC - DDC::0 - Computer science, information & general works::004 - Data processing, computer science
en_US
ethz.identifier.diss
29540
en_US
ethz.publication.place
Zurich
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09568 - Rätsch, Gunnar / Rätsch, Gunnar
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02150 - Dep. Informatik / Dep. of Computer Science::02661 - Institut für Maschinelles Lernen / Institute for Machine Learning::09568 - Rätsch, Gunnar / Rätsch, Gunnar
en_US
ethz.date.deposited
2023-11-29T21:57:14Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Embargoed
en_US
ethz.date.embargoend
2026-11-30
ethz.rosetta.installDate
2023-11-30T12:02:20Z
ethz.rosetta.lastUpdated
2024-02-03T07:47:05Z
ethz.rosetta.versionExported
true
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Publication type
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Doctoral Thesis [29839]