Using deep learning to model the hierarchical structure and function of a cell
Nature Methods 15, 290–298 (2018), published online 5 March 2018. The article page gives Received 19 July 2017, Accepted 7 February 2018, Published 5 March 2018, Issue date 1 April 2018.
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- No authorship claim recorded yet. The source record lists Jianzhu Ma, Michael Ku Yu, Samson Fong, Keiichiro Ono, Eric Sage, Barry Demchak, Roded Sharan and Trey Ideker.
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- Using deep learning to model the hierarchical structure and function of a cell Nature Methods · published 2018-03-05 · Retrieved for the candidate pack on 2026-10-30 (SHA-256 1a3adc811808c562cb9968e983cb46ee8a183a3b5846d2cf697baf4223bbf69b). Nature Methods article page as served on 2026-10-30; abstract and metadata, full text behind subscription.
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- AI models can simulate or predict the behaviour of cells (a "virtual cell")Ma et al. (Nature Methods, published online 5 March 2018) state that DCell, a "visible" neural network embedded in the hierarchical structure of the subsystems of a eukaryotic cell and trained on several million genotypes, simulates cellular growth nearly as accurately as laboratory observations.2018-03-05 · publication · Stated in source · Supported Inspect claim & evidence ↗AI models can simulate or predict the behaviour of cells (a "virtual cell"): propositions page →
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