Detecting Systematic Error
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My Basket My Account Bioinformatics About This Journal Contact This Journal how to reduce random error Subscriptions View Current Issue (Volume 32 Issue 20 October 15, systematic error examples 2016) Archive Search Oxford Journals Science & Mathematics Bioinformatics Volume 23 Issue 13 Pp. 1648-1657. An systematic error calculation efficient method for the detection and elimination of systematic error in high-throughput screening Vladimir Makarenkov1,*, Pablo Zentilli1, Dmytro Kevorkov1, Andrei Gagarin1, Nathalie Malo2,3 and Robert Nadon2,4
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1Department d’informatique, Université du Québec à Montreal, C.P.8888, s. Centre Ville, Montreal, QC, Canada, H3C 3P8, 2McGill University and Genome Quebec Innovation Centre, 740 Dr. Penfield Ave., Montreal, QC, Canada, H3A 1A4, 3Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, 1020 Pine Av. West, Montreal, QC, Canada, H3A 1A4 and 4Department random error calculation of Human Genetics, McGill University, 1205 Dr. Penfield Ave., N5/13, Montreal, QC, Canada, H3A 1B1 *To whom correspondence should be addressed. Received December 7, 2006. Revision received February 22, 2007. Accepted April 10, 2007. Next Section Abstract Motivation: High-throughput screening (HTS) is an early-stage process in drug discovery which allows thousands of chemical compounds to be tested in a single study. We report a method for correcting HTS data prior to the hit selection process (i.e. selection of active compounds). The proposed correction minimizes the impact of systematic errors which may affect the hit selection in HTS. The introduced method, called a well correction, proceeds by correcting the distribution of measurements within wells of a given HTS assay. We use simulated and experimental data to illustrate the advantages of the new method compared to other widely-used methods of data correction and hit selection in HTS. Contact: makarenkov.vladimir{at}uqam.ca Supplementary information: Supplementary data are available at Bioinfor
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ListBMC Bioinformaticsv.12; 2011PMC3034671 BMC Bioinformatics. 2011; 12: 25. Published online 2011 Jan 19. doi: 10.1186/1471-2105-12-25PMCID: PMC3034671Systematic error detection http://bioinformatics.oxfordjournals.org/content/23/13/1648.full in experimental high-throughput screeningPlamen Dragiev,1 Robert Nadon,2,3 and Vladimir Makarenkov11Département d'informatique, Université du Québec à Montréal, C.P. 8888 succ. Centre-Ville, Montreal (QC) H3C 3P8, Canada2Department of Human Genetics, McGill University, 1205 Dr. Penfield Ave. Montreal, http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3034671/ QC, H3A 1B13McGill University and Genome Quebec Innovation Centre, 740 Dr. Penfield Ave., Montreal, QC, H3A 1A4, CanadaCorresponding author.Plamen Dragiev: cc.nemalp@nemalp; Robert Nadon: ac.lligcm@nodan.trebor; Vladimir Makarenkov: ac.maqu@rimidalv.voknerakam Author information ► Article notes ► Copyright and License information ►Received 2010 Jun 30; Accepted 2011 Jan 19.Copyright ©2011 Dragiev et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been cited by other articles in PMC.AbstractBackgroundHigh-throughput screening (HTS) is a key part of the drug discover
organizational phenomenon, see systemic bias This article needs additional citations for verification. Please help improve this article by adding citations to reliable https://en.wikipedia.org/wiki/Systematic_error sources. Unsourced material may be challenged and removed. (September 2016) (Learn how and when to remove this template message) "Measurement error" redirects here. It is not to be confused with Measurement uncertainty. A scientist adjusts an atomic force microscopy (AFM) device, which is used to measure surface characteristics and imaging for semiconductor wafers, lithography systematic error masks, magnetic media, CDs/DVDs, biomaterials, optics, among a multitude of other samples. Observational error (or measurement error) is the difference between a measured value of quantity and its true value.[1] In statistics, an error is not a "mistake". Variability is an inherent part of things being measured and of the measurement process. Measurement detecting systematic error errors can be divided into two components: random error and systematic error.[2] Random errors are errors in measurement that lead to measurable values being inconsistent when repeated measures of a constant attribute or quantity are taken. Systematic errors are errors that are not determined by chance but are introduced by an inaccuracy (as of observation or measurement) inherent in the system.[3] Systematic error may also refer to an error having a nonzero mean, so that its effect is not reduced when observations are averaged.[4] Contents 1 Overview 2 Science and experiments 3 Systematic versus random error 4 Sources of systematic error 4.1 Imperfect calibration 4.2 Quantity 4.3 Drift 5 Sources of random error 6 Surveys 7 See also 8 Further reading 9 References Overview[edit] This article or section may need to be cleaned up. It has been merged from Measurement uncertainty. There are two types of measurement error: systematic errors and random errors. A systematic error (an estimate o