Biological shape analysis with geometric statistics and learning

dc.bibliographicCitation.seriesTitle	Snapshots of Modern Mathematics from Oberwolfach	eng
dc.bibliographicCitation.volume	8/2022
dc.contributor.author	Utpala, Saiteja
dc.contributor.author	Miolane, Nina
dc.date.accessioned	2024-10-16T13:55:11Z
dc.date.available	2024-10-16T13:55:11Z
dc.date.issued	2022
dc.description.abstract	The advances in biomedical imaging techniques have enabled us to access the 3D shapes of a variety of structures: organs, cells, proteins. Since biological shapes are related to physiological functions, shape data may hold the key to unlocking outstanding mysteries in biomedicine. This snapshot introduces the mathematical framework of geometric statistics and learning and its applications to biomedicine.
dc.description.version	publishedVersion
dc.identifier.uri	https://oa.tib.eu/renate/handle/123456789/16864
dc.identifier.uri	https://doi.org/10.34657/15886
dc.language.iso	eng
dc.publisher	Oberwolfach : Mathematisches Forschungsinstitut Oberwolfach gGmbH
dc.relation.doi	https://doi.org/10.14760/SNAP-2022-008-EN
dc.relation.essn	2626-1995
dc.rights.license	CC BY-SA 4.0 Unported
dc.rights.uri	http://creativecommons.org/licenses/by-sa/4.0/
dc.subject.ddc	510
dc.subject.other	Geometry and Topology
dc.subject.other	Probability Theory and Statistics
dc.title	Biological shape analysis with geometric statistics and learning
dc.type	Report
dc.type	Text

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