The identification of cis-regulatory elements: A review from a machine learning perspective

BioSystems

Volumn 138, Issue , 2015, Pages 6-17

  Li, Yifeng ^a,b   Chen, Chih yu ^a   Kaye, Alice M ^a   Wasserman, Wyeth W ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^b NATIONAL RESEARCH COUNCIL CANADA   (Canada)

Author keywords

Cis regulatory elements; Data integration; Deep learning; Enhancers; Ensemble learning; Gene regulation; Machine learning; Promoters

Indexed keywords

TRANSCRIPTION FACTOR;

CELLS AND CELL COMPONENTS; COMPLEXITY; CYTOGENETICS; DATA ASSIMILATION; DNA; GENE EXPRESSION; GENOME; GENOMICS; MACHINE LEARNING; PHENOTYPE;

ARTIFICIAL NEURAL NETWORK; BAYESIAN LEARNING; BINDING SITE; DNA SEQUENCE; ENHANCER REGION; GENE REGULATORY NETWORK; HIDDEN MARKOV MODEL; HUMAN; KERNEL METHOD; MACHINE LEARNING; NEXT GENERATION SEQUENCING; PROMOTER REGION; RANDOM FOREST; REVIEW; SAMPLE SIZE; STATISTICAL MODEL; ANIMAL; AUTOMATED PATTERN RECOGNITION; CHROMOSOMAL MAPPING; GENETICS; MOLECULAR GENETICS; NUCLEOTIDE SEQUENCE; PROCEDURES; REGULATORY SEQUENCE; SEQUENCE ALIGNMENT;

ANIMALS; BASE SEQUENCE; CHROMOSOME MAPPING; HUMANS; MACHINE LEARNING; MOLECULAR SEQUENCE DATA; PATTERN RECOGNITION, AUTOMATED; REGULATORY SEQUENCES, NUCLEIC ACID; SEQUENCE ALIGNMENT; SEQUENCE ANALYSIS, DNA;

EID: 84959350566     PISSN: 03032647     EISSN: 18728324     Source Type: Journal    
DOI: 10.1016/j.biosystems.2015.10.002     Document Type: Review

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