dc.contributor.author |
Pfeifer, Nico |
|
dc.contributor.author |
Lerche, Holger |
|
dc.contributor.author |
Hedrich, Ulrike B. S. |
|
dc.contributor.author |
Boßelmann, Christian Malte |
|
dc.date.accessioned |
2023-11-30T08:20:39Z |
|
dc.date.available |
2023-11-30T08:20:39Z |
|
dc.date.issued |
2023-03-06 |
|
dc.identifier.uri |
http://hdl.handle.net/10900/148242 |
|
dc.language.iso |
en |
de_DE |
dc.publisher |
PLOS |
de_DE |
dc.relation.uri |
https://doi.org/10.1371/journal.pcbi.1010959 |
de_DE |
dc.subject.ddc |
004 |
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.personen.roh |
Pfeifer, Nico |
|
utue.personen.roh |
Lerche, Holger |
|
utue.personen.roh |
Hedrich, Ulrike B. S. |
|
utue.personen.roh |
Boßelmann, Christian Malte |
|
dcterms.isPartOf.ZSTitelID |
PLoS Computational Biology |
de_DE |