A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action

PLoS Computational Biology

Volumn 13, Issue 10, 2017, Pages

  Abadi, Shiran ^a   Yan, Winston X ^b,c   Amar, David ^a,d   Mayrose, Itay ^a  

^a TEL AVIV UNIVERSITY   (Israel)

^b BROAD INSTITUTE OF MIT AND HARVARD   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; EFFICIENCY; GENES; GENOME; LEARNING SYSTEMS;

'CURRENT; CLEAVAGE EFFICIENCY; CLEAVAGE PATTERNS; GENOMIC REGIONS; GENOMICS; MACHINE LEARNING APPROACHES; MECHANISM OF ACTION; SEQUENCE FEATURES; SEQUENCE HOMOLOGY; TARGET SITES;

FORECASTING;

CRISPR ASSOCIATED PROTEIN; RNA; GUIDE RNA;

ALGORITHM; ARTICLE; CONCEPTUAL FRAMEWORK; CRISPR TARGET ASSESSMENT; CRISPR-CAS9 SYSTEM; DNA CLEAVAGE; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; MACHINE LEARNING; METHODOLOGY; PREDICTION; PRODUCTIVITY; THERMODYNAMICS; VALIDATION PROCESS; BIOLOGY; CRISPR CAS SYSTEM; GENE EDITING; GENETICS; HUMAN; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC;

ALGORITHMS; COMPUTATIONAL BIOLOGY; CRISPR-CAS SYSTEMS; GENE EDITING; HUMANS; MACHINE LEARNING; RNA, GUIDE; ROC CURVE;

EID: 85032645372     PISSN: 1553734X     EISSN: 15537358     Source Type: Journal    
DOI: 10.1371/journal.pcbi.1005807     Document Type: Article

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