Systematic error detection in experimental high-throughput screening

Background: High-throughput screening (HTS) is a key part of the drug discovery process during which thousands of chemical compounds are screened and their activity levels measured in order to identify potential drug candidates (i.e., hits). Many technical, procedural or environmental factors can cause systematic measurement error or inequalities in the conditions in which the measurements are taken. Such systematic error has the potential to critically affect the hit selection process. Several error correction methods and software have been developed to address this issue in the context of experimental HTS [1-7]. Despite their power to reduce the impact of systematic error when applied to error perturbed datasets, those methods also have one disadvantage - they introduce a bias when applied to data not containing any systematic error [6]. Hence, we need first to assess the presence of systematic error in a given HTS assay and then carry out systematic error correction method if and o...

• Type: Journal
• Year: 2011
• Where: BMCBI
• Authors: Plamen Dragiev, Robert Nadon, Vladimir Makarenkov
• Keywords: Artificial Intelligence, BMCBI 2011, Error Correction Method, Systematic Error, Systematic Error Correction