A modular approach for integrative analysis of large-scale gene-expression and drug-response data

Détails

ID Serval
serval:BIB_2313B80EF6CC
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
A modular approach for integrative analysis of large-scale gene-expression and drug-response data
Périodique
Nature Biotechnology
Auteur(s)
Kutalik Z., Beckmann J. S., Bergmann S.
ISSN
1546-1696
Statut éditorial
Publié
Date de publication
2008
Peer-reviewed
Oui
Volume
26
Numéro
5
Pages
531-539
Langue
anglais
Résumé
High-throughput technologies are now used to generate more than one type of data from the same biological samples. To properly integrate such data, we propose using co-modules, which describe coherent patterns across paired data sets, and conceive several modular methods for their identification. We first test these methods using in silico data, demonstrating that the integrative scheme of our Ping-Pong Algorithm uncovers drug-gene associations more accurately when considering noisy or complex data. Second, we provide an extensive comparative study using the gene-expression and drug-response data from the NCI-60 cell lines. Using information from the DrugBank and the Connectivity Map databases we show that the Ping-Pong Algorithm predicts drug-gene associations significantly better than other methods. Co-modules provide insights into possible mechanisms of action for a wide range of drugs and suggest new targets for therapy
Mots-clé
administration & dosage , Algorithms , analysis , Biological Assay , Cell Line , Computer Simulation , drug effects , Drug Evaluation,Preclinical , Gene Expression , Gene Expression Profiling , genetics , methods , Models,Biological , Pharmaceutical Preparations , Signal Transduction , Switzerland , Systems Integration , therapy
Pubmed
Web of science
Création de la notice
29/01/2009 22:14
Dernière modification de la notice
20/08/2019 13:00
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