Biological shape analysis with geometric statistics and learning

dc.contributor.author	Utpala, Saiteja
dc.contributor.author	Miolane, Nina
dc.contributor.editor	Munday, Sara
dc.contributor.editor	Randecker, Anja
dc.date.accessioned	2022-10-25T09:28:10Z
dc.date.available	2022-10-25T09:28:10Z
dc.date.issued	2022-10-25
dc.identifier.uri	http://publications.mfo.de/handle/mfo/3985
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.	en_US
dc.language.iso	en	en_US
dc.publisher	Mathematisches Forschungsinstitut Oberwolfach	en_US
dc.relation.ispartofseries	Snapshots of modern mathematics from Oberwolfach;2022-08
dc.rights	Attribution-ShareAlike 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-sa/4.0/	*
dc.title	Biological shape analysis with geometric statistics and learning	en_US
dc.type	Article	en_US
dc.identifier.doi	10.14760/SNAP-2022-008-EN
local.series.id	SNAP-2022-008-EN	en_US
local.subject.snapshot	Geometry and Topology	en_US
local.subject.snapshot	Probability Theory and Statistics	en_US
dc.identifier.urn	urn:nbn:de:101:1-2022112209312693372541
dc.identifier.ppn	1823161618

Except where otherwise noted, this item's license is described as Attribution-ShareAlike 4.0 International