Abstract
High throughput screening (HTS) is a widely used effective approach in genome-wide association and large scale protein expression studies, drug discovery, and biomedical imaging research. How to accurately identify candidate 'targets' or biologically meaningful features with a high degree of confidence has led to extensive statistical research in an effort to minimize both false-positive and false-negative rates. A large body of literature on this topic with in-depth statistical contents is available. We examine currently available statistical methods on HTS and aim to summarize some selected methods into a concise, easy-tofollow introduction for experimental biologists.
Original language | English (US) |
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Pages (from-to) | 354-360 |
Number of pages | 7 |
Journal | Frontiers of Biology in China |
Volume | 5 |
Issue number | 4 |
DOIs | |
State | Published - 2010 |
Keywords
- false-negative rate
- false-positive rate
- high throughput screen
- predictive modeling
- target discovery
ASJC Scopus subject areas
- Biotechnology
- Ecology, Evolution, Behavior and Systematics
- Ecology
- Genetics