Predicting functional effects of ion channel variants using new phenotypic machine learning methods

dc.contributor.author	Hedrich, Ulrike B. S.
dc.contributor.author	Lerche, Holger
dc.contributor.author	Pfeifer, Nico
dc.contributor.author	Boßelmann, Christian Malte
dc.date.accessioned	2023-08-21T05:01:52Z
dc.date.available	2023-08-21T05:01:52Z
dc.date.issued	2023
dc.identifier.issn	1553-734X
dc.identifier.uri	http://hdl.handle.net/10900/144426
dc.language.iso	en	de_DE
dc.publisher	Public Library Science	de_DE
dc.relation.uri	http://dx.doi.org/10.1371/journal.pcbi.1010959	de_DE
dc.subject.ddc	570	de_DE
dc.subject.ddc	600	de_DE
dc.title	Predicting functional effects of ion channel variants using new phenotypic machine learning methods	de_DE
dc.type	Article	de_DE
utue.quellen.id	20230619000000_00910
utue.personen.roh	Bosselmann, Christian Malte
utue.personen.roh	Hedrich, Ulrike B. S.
utue.personen.roh	Lerche, Holger
utue.personen.roh	Pfeifer, Nico
dcterms.isPartOf.ZSTitelID	Plos Computational Biology	de_DE
dcterms.isPartOf.ZS-Issue	Article e1010959	de_DE
dcterms.isPartOf.ZS-Volume	19 (3)	de_DE
utue.fakultaet	04 Medizinische Fakultät	de_DE
utue.fakultaet	07 Mathematisch-Naturwissenschaftliche Fakultät	de_DE

Files in this item

Files	Size	Format	View
There are no files associated with this item.