Image-based multivariate profiling of drug responses from single cells

By Lit-Hsin Loo, Lani F. Wu, Steven J. Altschuler. Nature Methods, 2007

335 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/bfc8a8724b36cd2b1d068d1f997400e74791a68d

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Citations in the corpus

Machine learning and computer vision approaches for phenotypic profiling (2017, background): “For example, Loo et al. (2007) selected a subset of informative features by iteratively removing other subsets that did not affect downstream performance of their classifier.”
High-Content Screening for Quantitative Cell Biology. (2016, background): “Classi ﬁ - cation accuracy can also be implemented as a measure for identifying when a treatment condition is signi ﬁ cantly different from the control, an approach that has been used to quantitatively assess drug titrations in human cells [43].”
Computer vision for high content screening (2016, methodology): “…studies of protein localization in yeast (Breker et al ., 2013; Chong et al ., 2015; Tkach et al ., 2012), drug response screens in mammalian cells (Ljosa et al ., 2013; Loo et al ., 2007; Perlman et al ., 2004), and cell morphology characterization (Vizeacoumar et al ., 2010; Yin et al ., 2013).”
EBImage—an R package for image processing with applications to cellular phenotypes (2010, methodology): “Availability: EBImage is free and open source, released under the LGPL license and available from the Bioconductor project (http:// www.bioconductor.org/packages/release/bioc/html/EBImage.html).”
Integrating high-content screening and ligand-target prediction to identify mechanism of action. (2008, methodology): “Despite progress on analysis of HCS data reported here and elsewher”
Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis[W][OPEN] (2014, methodology): “…comprehensively the ability of these traits to re ﬂ ect the responses to the external treatment, we adopted a support vector machine (SVM)-based approach (Loo et al., 2007; Iyer-Pascuzzi et al., 2010), in which “ optimal ” hyperplanes separate treated and untreated samples (Supplemental Figure 5A).”
Biological Imaging Software Tools (2012, background): “Examples include dose-response curves (responses to a drug at increasing concentrations) 47 and time points in a time series 44 .”
Multiple Nuclei Tracking Using Integer Programming for Quantitative Cancer Cell Cycle Analysis (2010, background): “However, identifying effective small molecules or anti-mitotic drugs such as Taxanes from a large number of compounds available today is a daunting task [3], [4].”
Machine learning in cell biology – teaching computers to recognize phenotypes (2013, background): “For instance, support vector machines (discriminative approach) are widely used in cell biology (Meyer et al., 2003; Loo et al., 2007; Fuchs et al., 2010; Held et al., 2010; Neumann et al., 2010) owing to their good average performance among benchmark data sets (Meyer et al., 2003) and…”
Scoring diverse cellular morphologies in image-based screens with iterative feedback and machine learning (2009, background): “Previous approaches (15, 19, 20) have succeeded on highly penetrant phenotypes where positive control samples are known, but none of the phenotypes in our study had positive control samples available, and most were low-penetrance.”