Reference : Drug Target Prediction Using Context-Specific Metabolic Models Reconstructed from rFA...

	Document type :	Parts of books : Contribution to collective works
	Discipline(s) :	Life sciences : Multidisciplinary, general & others
	Focus Areas :	Systems Biomedicine
	To cite this reference:	http://hdl.handle.net/10993/52300

Title :	Drug Target Prediction Using Context-Specific Metabolic Models Reconstructed from rFASTCORMICS.
Language :	English
Author, co-author :	Bintener, Tamara [> >]
	Pires Pacheco, Maria Irene [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM) >]
	Kishk, Ali [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
	Didier, Jeff [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
	Sauter, Thomas [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Life Sciences and Medicine (DLSM)]
Publication date :	2022
Main document title :	Methods in Molecular Biology
Publisher :	Springer
Pages :	221-240
Peer reviewed :	Yes
City :	Clifton, N.J.
Country :	United States
Keywords :	[en] Antineoplastic Agents/therapeutic use ; Computational Biology ; Drug Repositioning ; Humans ; Neoplasms/drug therapy ; Workflow ; Cancer ; Drug prediction ; Drug repurposing ; Metabolic Modelling
Abstract :	[en] Metabolic modeling is a powerful computational tool to analyze metabolism. It has not only been used to identify metabolic rewiring strategies in cancer but also to predict drug targets and candidate drugs for repurposing. Here, we will elaborate on the reconstruction of context-specific metabolic models of cancer using rFASTCORMICS and the subsequent prediction of drugs for repurposing using our drug prediction workflow.
Target :	Researchers ; Students
Permalink :	http://hdl.handle.net/10993/52300
DOI :	10.1007/978-1-0716-2513-2_17
Commentary :	© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

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