Applications in image-based profiling of perturbations.

By Juan C. Caicedo, Shantanu Singh, Anne E Carpenter. 2016

160 citations recorded by the source corpus.

A dramatic shift has occurred in how biologists use microscopy images. Whether experiments are small-scale or high-throughput, automatically quantifying biological properties in images is now widespread. We see yet another revolution under way: a transition towards using automated image analysis to not only identify phenotypes a biologist specifically seeks to measure ('screening') but also as an unbiased and sensitive tool to capture a wide variety of subtle features of cell (or organism) state ('profiling'). Mapping similarities among samples using image-based (morphological) profiling has tremendous potential to transform drug discovery, functional genomics, and basic biological research. Applications include target identification, lead hopping, library enrichment, functionally annotating genes/alleles, and identifying small molecule modulators of gene activity and disease-specific phenotypes.

Subfields: Biophysics, Molecular Biology

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Citations in the corpus

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images (2018, methodology): “From image-based assays to high-content screening (1,2), microscopy images have led to understanding genetic perturbations, pursuing drug discovery, and phenotyping cells in biomedical applications and cell biology research (3,4).”
CytoGAN: Generative Modeling of Cell Images (2017, background): “Our work focuses on the task of morphological profiling, which aims to map microscopy images of cells to representation vectors that capture salient features and axes of variation in an unsupervised manner [4].”
Machine learning and computer vision approaches for phenotypic profiling (2017, methodology): “During the analysis of screening data, specific features relevant to the phenotype of interest are considered (Caicedo et al., 2016).”
Automating Morphological Profiling with Generic Deep Convolutional Networks (2016, background): “Such ‘unbiased’ representations of phenotype enable new analysis based on similarities and differences among chemical or genetic perturbations, which might lead to new insights in functional genomics, drug discovery and target identiﬁcation [1].”