New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells

Leonidou, Nantia; Renz, Alina; Mostolizadeh, Reihaneh; Draeger, Andreas

dc.contributor.author	Leonidou, Nantia
dc.contributor.author	Renz, Alina
dc.contributor.author	Mostolizadeh, Reihaneh
dc.contributor.author	Dräger, Andreas
dc.date.accessioned	2023-08-18T07:23:29Z
dc.date.available	2023-08-18T07:23:29Z
dc.date.issued	2023
dc.identifier.issn	1553-734X
dc.identifier.uri	http://hdl.handle.net/10900/144388
dc.language.iso	en	en
dc.publisher	Public Library Science	de_DE
dc.relation.uri	http://dx.doi.org/10.1371/journal.pcbi.1010903
dc.subject.ddc	570	de_DE
dc.subject.ddc	600	de_DE
dc.title	New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells	de_DE
dc.type	Article	de_DE
utue.quellen.id	20230619000000_00949
utue.personen.roh	Leonidou, Nantia
utue.personen.roh	Renz, Alina
utue.personen.roh	Mostolizadeh, Reihaneh
utue.personen.roh	Draeger, Andreas
dcterms.isPartOf.ZSTitelID	Plos Computational Biology	de_DE
dcterms.isPartOf.ZS-Issue	3	de_DE
dcterms.isPartOf.ZS-Volume	19	de_DE
utue.fakultaet	04 Medizinische Fakultät
utue.fakultaet	07 Mathematisch-Naturwissenschaftliche Fakultät
utue.fakultaet	Sonstige