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Robust Hit Identification by Quality Assurance and Multivariate Data Analysis of a High-Content, Cell-Based Assay
- Source :
- SLAS Discovery. 12:1042-1049
- Publication Year :
- 2007
- Publisher :
- Elsevier BV, 2007.
-
Abstract
- 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. (Journal of Biomolecular Screening 2007:1042-1049)
- Subjects :
- Reproducibility of results
0301 basic medicine
Computer science
Machine learning
computer.software_genre
Bioinformatics
01 natural sciences
Biochemistry
Bottleneck
Analytical Chemistry
03 medical and health sciences
Cluster analysis
Tissue array analysis
Neurites
Instrumentation (computer programming)
Hit selection
Throughput (business)
572: Biochemie
business.industry
Quality control
0104 chemical sciences
Hierarchical clustering
010404 medicinal & biomolecular chemistry
Identification (information)
030104 developmental biology
Multivariate analysis
High-content screening
Molecular Medicine
Artificial intelligence
business
computer
Quality assurance
Biotechnology
Subjects
Details
- ISSN :
- 24725552
- Volume :
- 12
- Database :
- OpenAIRE
- Journal :
- SLAS Discovery
- Accession number :
- edsair.doi.dedup.....994342bd77376266aadb358016655059