Literature DB >> 16103415

Statistical analysis of systematic errors in high-throughput screening.

Dmytro Kevorkov1, Vladimir Makarenkov.   

Abstract

High-throughput screening (HTS) is an efficient technology for drug discovery. It allows for screening of more than 100,000 compounds a day per screen and requires effective procedures for quality control. The authors have developed a method for evaluating a background surface of an HTS assay; it can be used to correct raw HTS data. This correction is necessary to take into account systematic errors that may affect the procedure of hit selection. The described method allows one to analyze experimental HTS data and determine trends and local fluctuations of the corresponding background surfaces. For an assay with a large number of plates, the deviations of the background surface from a plane are caused by systematic errors. Their influence can be minimized by the subtraction of the systematic background from the raw data. Two experimental HTS assays from the ChemBank database are examined in this article. The systematic error present in these data was estimated and removed from them. It enabled the authors to correct the hit selection procedure for both assays.

Mesh:

Year:  2005        PMID: 16103415     DOI: 10.1177/1087057105276989

Source DB:  PubMed          Journal:  J Biomol Screen        ISSN: 1087-0571


  11 in total

1.  Rank ordering plate data facilitates data visualization and normalization in high-throughput screening.

Authors:  Chand S Mangat; Amrita Bharat; Sebastian S Gehrke; Eric D Brown
Journal:  J Biomol Screen       Date:  2014-05-14

2.  Minimizing Systematic Errors in Quantitative High Throughput Screening Data Using Standardization, Background Subtraction, and Non-Parametric Regression.

Authors:  Mitas Ray; Keith Shockley; Grace Kissling
Journal:  J Exp Second Sci       Date:  2014-04

Review 3.  High-throughput screening for genes that prevent excess DNA replication in human cells and for molecules that inhibit them.

Authors:  Chrissie Y Lee; Ronald L Johnson; Jennifer Wichterman-Kouznetsova; Rajarshi Guha; Marc Ferrer; Pinar Tuzmen; Scott E Martin; Wenge Zhu; Melvin L DePamphilis
Journal:  Methods       Date:  2012-04-05       Impact factor: 3.608

4.  Optimization and application of median filter corrections to relieve diverse spatial patterns in microtiter plate data.

Authors:  Paul J Bushway; Behrad Azimi; Susanne Heynen-Genel
Journal:  J Biomol Screen       Date:  2011-09-06

5.  GUItars: a GUI tool for analysis of high-throughput RNA interference screening data.

Authors:  Asli N Goktug; Su Sien Ong; Taosheng Chen
Journal:  PLoS One       Date:  2012-11-20       Impact factor: 3.240

6.  Systematic error detection in experimental high-throughput screening.

Authors:  Plamen Dragiev; Robert Nadon; Vladimir Makarenkov
Journal:  BMC Bioinformatics       Date:  2011-01-19       Impact factor: 3.169

7.  Estimating Potency in High-Throughput Screening Experiments by Maximizing the Rate of Change in Weighted Shannon Entropy.

Authors:  Keith R Shockley
Journal:  Sci Rep       Date:  2016-06-15       Impact factor: 4.379

8.  Assay Establishment and Validation of a High-Throughput Screening Platform for Three-Dimensional Patient-Derived Colon Cancer Organoid Cultures.

Authors:  Karsten Boehnke; Philip W Iversen; Dirk Schumacher; María José Lallena; Rubén Haro; Joaquín Amat; Johannes Haybaeck; Sandra Liebs; Martin Lange; Reinhold Schäfer; Christian R A Regenbrecht; Christoph Reinhard; Juan A Velasco
Journal:  J Biomol Screen       Date:  2016-05-27

9.  Developing and validating predictive decision tree models from mining chemical structural fingerprints and high-throughput screening data in PubChem.

Authors:  Lianyi Han; Yanli Wang; Stephen H Bryant
Journal:  BMC Bioinformatics       Date:  2008-09-25       Impact factor: 3.169

10.  A cell-free fluorometric high-throughput screen for inhibitors of Rtt109-catalyzed histone acetylation.

Authors:  Jayme L Dahlin; Rondedrick Sinville; Jonathan Solberg; Hui Zhou; Junhong Han; Subhashree Francis; Jessica M Strasser; Kristen John; Derek J Hook; Michael A Walters; Zhiguo Zhang
Journal:  PLoS One       Date:  2013-11-18       Impact factor: 3.240

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