We are analyzing https://link.springer.com/article/10.1186/s13029-014-0027-x.

Title:
Robust dose-response curve estimation applied to high content screening data analysis | Source Code for Biology and Medicine
Description:
Background and method 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. Results and conclusion Automatic curve fitting of 19,236 dose-response experiments shows that our proposed method outperforms the current fitting methods provided by MATLAB®;’s nlinfit nlinfit function and GraphPad’s Prism software.
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Keywords {🔍}

fitting, data, curve, function, method, points, algorithm, outliers, figure, weighting, drc, results, nlinfit, robust, parameters, tukey, article, curves, matlab, analysis, detection, biweight, estimation, high, median, google, scholar, outlier, authors, doseresponse, number, automatic, based, content, prism, nonlinear, shows, point, sumofsquares, weights, applied, screening, error, korea, concentrations, information, full, response, initial, errors,

Topics {✒️}

high content screening konferenz der sas-anwender dose-response curve fitting parameter dose-response curve dose-response curve data jin yeop kim open access license genome-wide data analysis dose-response curves obtained article download pdf provided java-based software thuy tuong nguyen national research foundation dose-response curve dose response curve yong-jun kwon related subjects myungjoo kang high throughput fitting 𝑘 ≠ � dose-response curves 𝜎 𝑖 privacy choices/manage cookies full size image life science research authors’ original file cambridge university press outlier detection mechanism oxford university press institut pasteur korea small interfering rna curr chem genomics automated fitting procedure require proper fitting automatic outlier detection robust weighting function high steep slope 𝜕 𝛽 𝑘 full access function totally ignores automatic outlier handling quadratic curve fitting perform curve fitting nlinfit nlinfit function including relative weighting basic computation section author information authors weighting makes sense robust weighting functions outlier detection step

Schema {🗺️}

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         description: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. Automatic curve fitting of 19,236 dose-response experiments shows that our proposed method outperforms the current fitting methods provided by MATLAB®;’s nlinfit nlinfit function and GraphPad’s Prism software.
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      headline:Robust dose-response curve estimation applied to high content screening data analysis
      description: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. Automatic curve fitting of 19,236 dose-response experiments shows that our proposed method outperforms the current fitting methods provided by MATLAB®;’s nlinfit nlinfit function and GraphPad’s Prism software.
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         Dose response curve
         Curve fitting
         Sigmoidal function
         Weighting function
         Outlier detection
         Computer Appl. in Life Sciences
         Computational Biology/Bioinformatics
         Bioinformatics
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