Robust hit identification by quality assurance and multivariate data analysis of a high-content, cell-based assay.
J Biomol Screen
; 12(8): 1042-9, 2007 Dec.
Article
em En
| MEDLINE
| ID: mdl-18087069
Recent technological advances in high-content screening instrumentation have increased its ease of use and throughput, expanding the application of high-content screening to the early stages of drug discovery. However, high-content screens produce complex data sets, presenting a challenge for both extraction and interpretation of meaningful information. This shifts the high-content screening process bottleneck from the experimental to the analytical stage. In this article, the authors discuss different approaches of data analysis, using a phenotypic neurite outgrowth screen as an example. Distance measurements and hierarchical clustering methods lead to a profound understanding of different high-content screening readouts. In addition, the authors introduce a hit selection procedure based on machine learning methods and demonstrate that this method increases the hit verification rate significantly (up to a factor of 5), compared to conventional hit selection based on single readouts only.
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Base de dados:
MEDLINE
Assunto principal:
Neuritos
/
Análise Serial de Tecidos
Idioma:
En
Ano de publicação:
2007
Tipo de documento:
Article