dc.contributor.author |
Menden, Kevin |
|
dc.contributor.author |
Dalmia, Anupriya |
|
dc.contributor.author |
Heutink, Peter |
|
dc.contributor.author |
Bonn, Stefan |
|
dc.date.accessioned |
2021-01-18T06:40:55Z |
|
dc.date.available |
2021-01-18T06:40:55Z |
|
dc.date.issued |
2020 |
|
dc.identifier.issn |
2375-2548 |
|
dc.identifier.uri |
http://hdl.handle.net/10900/111587 |
|
dc.language.iso |
en |
de_DE |
dc.publisher |
Amer Assoc Advancement Science |
de_DE |
dc.relation.uri |
http://dx.doi.org/10.1126/sciadv.aba2619 |
de_DE |
dc.subject.ddc |
500 |
de_DE |
dc.title |
Deep learning-based cell composition analysis from tissue expression profiles |
de_DE |
dc.type |
Article |
de_DE |
utue.quellen.id |
20200929220116_00734 |
|
utue.personen.roh |
Menden, Kevin |
|
utue.personen.roh |
Marouf, Mohamed |
|
utue.personen.roh |
Oller, Sergio |
|
utue.personen.roh |
Dalmia, Anupriya |
|
utue.personen.roh |
Magruder, Daniel Sumner |
|
utue.personen.roh |
Kloiber, Karin |
|
utue.personen.roh |
Heutink, Peter |
|
utue.personen.roh |
Bonn, Stefan |
|
dcterms.isPartOf.ZSTitelID |
Science Advances |
de_DE |
dcterms.isPartOf.ZS-Issue |
Article eaba2619 |
de_DE |
dcterms.isPartOf.ZS-Volume |
6 |
de_DE |