Statistical practice in high-throughput screening data analysis

By N. Malo, James A. Hanley, S. Cerquozzi, J. Pelletier, Robert Nadon. Nature Biotechnology, 2006

741 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Image-based multivariate profiling of drug responses from single cells (2007, background): “The high well-to-well variability might be due in part to the microenvironment difference in each well on microtiter plate”
Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1 (2009, background): “(4) Normalization of enrichment scores: because genes with different num-”
Data-analysis strategies for image-based cell profiling (2017, background): “However, those cells may indicate a valid and relevant phenotype, a possibility that should be assessed carefully (described in ‘Cell-level quality control’).”
BET bromodomain inhibition as a therapeutic strategy to target c-Myc (2011, positive): “As illustrated in Figures 1G and 1H, shRNA constructs targeting each of the expressed BET bromodomains are identified as reducing INA-6 proliferation as shown by normalized B scores (Malo et al., 2006).”
PubChem3D: Biologically relevant 3-D similarity (2011, background): “Background Recent advances in combinatorial chemistry [1-6] and high-throughput screening technology [7-17] have made the synthesis and screening of diverse chemical compounds easier, helping to create a demand in the biomedical research community for archives of publicly available screening data.”
CellProfiler Analyst: data exploration and analysis software for complex image-based screens (2008, methodology): “, for normalizing screen data [9,10]) to the system.”