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- W4313700805 endingPage "554" @default.
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- W4313700805 abstract "Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal 'hit' compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD)." @default.
- W4313700805 created "2023-01-08" @default.
- W4313700805 creator A5065193513 @default.
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- W4313700805 creator A5091391918 @default.
- W4313700805 date "2023-07-01" @default.
- W4313700805 modified "2023-10-14" @default.
- W4313700805 title "Deep learning in image-based phenotypic drug discovery" @default.
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- W4313700805 doi "https://doi.org/10.1016/j.tcb.2022.11.011" @default.
- W4313700805 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36623998" @default.
- W4313700805 hasPublicationYear "2023" @default.
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