Predicting effects of noncoding variants with deep learning-based sequence model

Nature Methods

Volumn 12, Issue 10, 2015, Pages 931-934

  Zhou, Jian ^a   Troyanskaya, Olga G ^a,b,c  

^a PRINCETON UNIVERSITY   (United States)

^b Princeton University   (United States)

^c Simons Center for Data Analysis   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DEOXYRIBONUCLEASE I; HISTONE; TRANSCRIPTION FACTOR GATA 1; CHROMATIN; FOXA1 PROTEIN, HUMAN; HEPATOCYTE NUCLEAR FACTOR 3ALPHA; TRANSCRIPTION FACTOR; UNTRANSLATED RNA;

ALLELIC IMBALANCE; ARTICLE; BINDING SITE; CHROMATIN; CHROMATIN IMMUNOPRECIPITATION; CLASSIFIER; COMPARATIVE STUDY; COMPUTER MODEL; CONTROLLED STUDY; DEEP LEARNING BASED SEQUENCE ANALYZER; DNA SEQUENCE; EPIGENETICS; GENETIC ANALYZER; GENETIC ASSOCIATION; GENETIC CONSERVATION; GENETIC MODEL; GENETIC VARIABILITY; HISTONE MODIFICATION; HUMAN; MUTAGENESIS; PREDICTION; PRIORITY JOURNAL; QUANTITATIVE TRAIT LOCUS; RECEIVER OPERATING CHARACTERISTIC; REGULATORY SEQUENCE; SEQUENCE ANALYSIS; SINGLE NUCLEOTIDE POLYMORPHISM; ALGORITHM; BIOLOGICAL MODEL; GENETICS; HUMAN GENOME; METABOLISM; MUTATION; SUPPORT VECTOR MACHINE;

ALGORITHMS; CHROMATIN; EPIGENOMICS; GENOME, HUMAN; HEPATOCYTE NUCLEAR FACTOR 3-ALPHA; HUMANS; MODELS, GENETIC; MUTATION; POLYMORPHISM, SINGLE NUCLEOTIDE; QUANTITATIVE TRAIT LOCI; REGULATORY SEQUENCES, NUCLEIC ACID; RNA, UNTRANSLATED; SUPPORT VECTOR MACHINE; TRANSCRIPTION FACTORS;

EID: 84958257565     PISSN: 15487091     EISSN: 15487105     Source Type: Journal    
DOI: 10.1038/nmeth.3547     Document Type: Article

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