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Automated image analysis: Improving accuracy and productivity in drug discovery
Author(s) -
Paul Ellwood
Publication year - 2003
Publication title -
the biochemist
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.126
H-Index - 7
eISSN - 1740-1194
pISSN - 0954-982X
DOI - 10.1042/bio02504028
Subject(s) - automation , productivity , drug discovery , data science , computer science , microbiology and biotechnology , risk analysis (engineering) , drug candidate , proteomics , drug , computational biology , engineering , business , bioinformatics , medicine , biology , pharmacology , mechanical engineering , biochemistry , gene , economics , macroeconomics
The Biochemist — August 2003. © 2003 The Biochemical Society 28 this is 216=65 536 levels of grey. The higher the number of grey levels, the greater the dynamic range of the camera. In simple terms, a good dynamic range allows real-time image capture, but shows such low noise levels that it can acquire images over long periods. Therefore, it is ideal for quantifying chemiluminescent band images because it is easier to distinguish between faint bands and brighter ones. The system’s dynamic range is also important because 16-bit images provide 65536 grey levels, which translate to a dynamic range of 4.8 orders of magnitude, more than double that of photographic film. This higher level of accuracy gives greater confidence when performing quantitative studies. The applications of automated image analysis for drug discovery are vast and examples are presented in this article. Automated image analysis: a versatile technology

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