DanQ: A hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

Nucleic Acids Research

Volumn 44, Issue 11, 2016, Pages

  Quang, Daniel ^a,b   Xie, Xiaohui ^a,b  

^a Irvine   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER MODEL; COMPUTER PROGRAM; CONTROLLED STUDY; DNA DETERMINATION; DNA SEQUENCE; GENE FUNCTION; HUMAN; INTERMETHOD COMPARISON; NONHUMAN; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; PROCEDURES; QUANTITATIVE TRAIT LOCUS; RECEIVER OPERATING CHARACTERISTIC; SINGLE NUCLEOTIDE POLYMORPHISM; SOFTWARE; WEB BROWSER;

DNA;

DNA; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; NEURAL NETWORKS (COMPUTER); POLYMORPHISM, SINGLE NUCLEOTIDE; QUANTITATIVE TRAIT LOCI; ROC CURVE; SEQUENCE ANALYSIS, DNA; SOFTWARE; WEB BROWSER;

EID: 84976413226     PISSN: 03051048     EISSN: 13624962     Source Type: Journal    
DOI: 10.1093/nar/gkw226     Document Type: Article

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