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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. Copy claim link
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Machine-checked, not reviewed by an editor Stated in source Supported
Supporting evidence: primary · 1 source
Date 2018-03-05 · publication Records dated 2018 → Role Articulation Register (kind of text) Nonfiction Scope of the passage Stated of growth prediction from genotype in one eukaryotic cell model. Scope and limits The record documents what this text states, in its register; it is not evidence that the proposition is true and implies no influence on or from any other text. The authors’ own report for one organism’s growth phenotype; the abstract does not use the phrase “virtual cell”. The research pack headlined it as earliest located; the cross-family check located an earlier text (1990), which holds the role (maintainer decision of 2026-10-30).
The evidence 02 primary source Supports the claim
Using deep learning to model the hierarchical structure and function of a cell ↗ Nature Methods · 2018-03-05
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Source excerpt “Trained on several million genotypes, DCell simulates cellular growth nearly as accurately as laboratory observations.” Location in source Abstract
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Machine-checked · 2026-10-30
Source details and check notes Authors Jianzhu Ma; Michael Ku Yu; Samson Fong; Keiichiro Ono; Eric Sage; Barry Demchak; Roded Sharan; Trey Ideker Version 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. Canonical address Open the canonical source ↗ Accessed 2026-10-30 Language English (en) Attribution Jianzhu Ma, Michael Ku Yu, Samson Fong, Keiichiro Ono, Eric Sage, Barry Demchak, Roded Sharan, Trey Ideker, “Using deep learning to model the hierarchical structure and function of a cell”, 2018-03-05. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI in biology (research/ai-in-biology). The quotation (15 words) matched the text retrieved for the candidate pack on 2026-10-30. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. The authors' stated result. A cross-family machine check (2026-10-30, item A55) answered the record’s question blind from fresh retrievals and located an earlier text; the record was corrected at admission. No human editorial review. primary source Supports the claim
Using deep learning to model the hierarchical structure and function of a cell ↗ Nature Methods · 2018-03-05
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Source excerpt “Here we develop DCell, a VNN embedded in the hierarchical structure of 2,526 subsystems comprising a eukaryotic cell” Location in source Abstract
How it was read Retrieved directly from the publisher
Machine-checked · 2026-10-30
Source details and check notes Authors Jianzhu Ma; Michael Ku Yu; Samson Fong; Keiichiro Ono; Eric Sage; Barry Demchak; Roded Sharan; Trey Ideker Version 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. Canonical address Open the canonical source ↗ Accessed 2026-10-30 Language English (en) Attribution Jianzhu Ma, Michael Ku Yu, Samson Fong, Keiichiro Ono, Eric Sage, Barry Demchak, Roded Sharan, Trey Ideker, “Using deep learning to model the hierarchical structure and function of a cell”, 2018-03-05. Reuse No open redistribution licence asserted. Bibliographic metadata, editorial paraphrases and short attributed quotations (at most 50 words each). Recheck terms before wider redistribution. Check notes Candidate pack passage, AI in biology (research/ai-in-biology). The quotation (18 words) matched the text retrieved for the candidate pack on 2026-10-30. Matching ignored differences of whitespace, case, quotation marks, dashes and Unicode compatibility forms, so the source’s typography can differ. Identifies the model as a neural network (VNN, visible neural network) of a cell. A cross-family machine check (2026-10-30, item A55) answered the record’s question blind from fresh retrievals and located an earlier text; the record was corrected at admission. No human editorial review. Status and meaning Editorial status. Not reviewed by an editor . An exploratory record that no editor has accepted. This status does not say whether a person has read any linked passage. Each evidence record above shows how it was checked.
Relation. A passage of the document meets the proposition’s criterion. The record documents what the text states, in its register (fiction and satire are labelled); it is not evidence that the proposition is true and implies no influence on or from another text.
Date. Online publication date printed on the article page.
Record ID aib-arg-p2-ma-2018