Using deep learning to model the hierarchical structure and function of a cell

Nature Methods

Volumn 15, Issue 4, 2018, Pages 290-298

  Ma, Jianzhu ^a   Yu, Michael Ku ^a,b   Fong, Samson ^a,c   Ono, Keiichiro ^a   Sage, Eric ^a   Demchak, Barry ^a   Sharan, Roded ^d   Ideker, Trey ^a,b,c  

^a UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c UNIVERSITY OF CALIFORNIA SAN DIEGO   (United States)

^d TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BRAIN FUNCTION; CELL FUNCTION; CELL GROWTH; CELL STRUCTURE; COMPUTER MODEL; DEEP NEURAL NETWORK; EUKARYOTIC CELL; GENE ONTOLOGY; GENOTYPE PHENOTYPE CORRELATION; NERVE CELL NETWORK; NONHUMAN; PRIORITY JOURNAL; REFERENCE DATABASE; SACCHAROMYCES CEREVISIAE; VISIBLE NEURAL NETWORK; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; GENE EXPRESSION REGULATION; GENOTYPE; HUMAN;

CELL PHYSIOLOGICAL PHENOMENA; COMPUTER SIMULATION; DEEP LEARNING; GENE EXPRESSION REGULATION; GENOTYPE; HUMANS; NEURAL NETWORKS (COMPUTER);

EID: 85044948558     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.4627     Document Type: Article

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