Unleashing high content screening in hit detection – Benchmarking AI workflows including novelty detection

Kupczyk, Erwin; Schorpp, Kenji; Hadian, Kamyar; Lin, Sean; Tziotis, Dimitrios; Schmitt‐Kopplin, Philippe; Mueller, Constanze

doi:10.1016/j.csbj.2022.09.023

Cited by 2 publications

(1 citation statement)

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“…While supervised ML models learn from previously defined categories, an outlier test such as novelty detection (ND) instead recognizes ‘novel’ (unknown) patterns, thus predicting whether compounds exhibit any biological activity. 73 For this, they used Cell Painting features for 641 validated and highly selective pharmaceutically relevant inhibitors over 123 targets to train models and they further used two additional validation sets (one for compounds that affect cell cycle and another with staurosporines). They found that a ND algorithm with an ensemble of classical ML algorithms was suitable to search for new active compounds.…”

Section: Discussionmentioning

confidence: 99%

A Decade in a Systematic Review: The Evolution and Impact of Cell Painting

Seal,

Trapotsi,

Spjuth

et al. 2024

Preprint

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High-content image-based assays have fueled significant discoveries in the life sciences in the past decade (2013-2023), including novel insights into disease etiology, mechanism of action, new therapeutics, and toxicology predictions. Here, we systematically review the substantial methodological advancements and applications of Cell Painting. Advancements include improvements in the Cell Painting protocol, assay adaptations for different types of perturbations and applications, and improved methodologies for feature extraction, quality control, and batch effect correction. Moreover, machine learning methods recently surpassed classical approaches in their ability to extract biologically useful information from Cell Painting images. Cell Painting data have been used alone or in combination with other - omics data to decipher the mechanism of action of a compound, its toxicity profile, and many other biological effects. Overall, key methodological advances have expanded Cell Painting’s ability to capture cellular responses to various perturbations. Future advances will likely lie in advancing computational and experimental techniques, developing new publicly available datasets, and integrating them with other high-content data types.

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Section: Discussionmentioning

confidence: 99%