Title |
Robust dose-response curve estimation applied to high content screening data analysis
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Published in |
Source Code for Biology and Medicine, December 2014
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DOI | 10.1186/s13029-014-0027-x |
Pubmed ID | |
Authors |
Thuy Tuong Nguyen, Kyungmin Song, Yury Tsoy, Jin Yeop Kim, Yong-Jun Kwon, Myungjoo Kang, Michael Adsetts Edberg Hansen |
Abstract |
Successfully automated sigmoidal curve fitting is highly challenging when applied to large data sets. In this paper, we describe a robust algorithm for fitting sigmoid dose-response curves by estimating four parameters (floor, window, shift, and slope), together with the detection of outliers. We propose two improvements over current methods for curve fitting. The first one is the detection of outliers which is performed during the initialization step with correspondent adjustments of the derivative and error estimation functions. The second aspect is the enhancement of the weighting quality of data points using mean calculation in Tukey's biweight function. |
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United Kingdom | 1 | 14% |
Unknown | 6 | 86% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 6 | 86% |
Scientists | 1 | 14% |
Mendeley readers
Geographical breakdown
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Unknown | 29 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 14 | 48% |
Student > Ph. D. Student | 6 | 21% |
Other | 2 | 7% |
Student > Master | 2 | 7% |
Student > Doctoral Student | 1 | 3% |
Other | 2 | 7% |
Unknown | 2 | 7% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 9 | 31% |
Engineering | 6 | 21% |
Biochemistry, Genetics and Molecular Biology | 3 | 10% |
Chemistry | 2 | 7% |
Business, Management and Accounting | 1 | 3% |
Other | 5 | 17% |
Unknown | 3 | 10% |