Deep generative models of genetic variation capture the effects of mutations

Nature Methods

Volumn 15, Issue 10, 2018, Pages 816-822

  Riesselman, Adam J ^a   Ingraham, John B ^a   Marks, Debora S ^a  

^a HARVARD MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACID; KANAMYCIN KINASE; POLYADENYLIC ACID BINDING PROTEIN;

ARTICLE; BAYESIAN LEARNING; CODON USAGE; GENE MUTATION; GENE SEQUENCE; GENETIC MODEL; GENETIC VARIATION; HUMAN; LEARNING ALGORITHM; NONHUMAN; PDZ DOMAIN; PRIORITY JOURNAL; PROBABILITY; RNA RECOGNITION MOTIF; ALGORITHM; BIOLOGY; HIGH THROUGHPUT SEQUENCING; MOLECULAR EVOLUTION; MUTATION; PROCEDURES; THEORETICAL MODEL;

ALGORITHMS; COMPUTATIONAL BIOLOGY; EVOLUTION, MOLECULAR; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MODELS, THEORETICAL; MUTATION;

EID: 85053833913     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/s41592-018-0138-4     Document Type: Article

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