ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants

American Journal of Human Genetics

Volumn 103, Issue 4, 2018, Pages 474-483

  Alirezaie, Najmeh ^a   Kernohan, Kristin D ^b   Hartley, Taila ^b   Majewski, Jacek ^a   Hocking, Toby Dylan ^a  

^a MCGILL UNIVERSITY   (Canada)

^b CHILDREN S HOSPITAL OF EASTERN ONTARIO   (Canada)

Author keywords

cancer; computational biology; diagnostic; machine learning; pathogenicity prediction; predictive modeling; rare disease; variant interpretation; whole exome sequencing

Indexed keywords

AREA UNDER THE CURVE; ARTICLE; CFOREST; CLINPRED SCORE; CLINVAR DATABASE; CONTROLLED STUDY; DATA BASE; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FALSE POSITIVE RESULT; FATHMM MKL; GENE FREQUENCY; HUMAN; INTERMETHOD COMPARISON; M CAP; MACHINE LEARNING; METALR; PATHOGENICITY; PREDICTION; PRIORITY JOURNAL; REVEL; SENSITIVITY AND SPECIFICITY; SINGLE NUCLEOTIDE POLYMORPHISM; XGBOOST; ALGORITHM; BIOLOGY; DISEASES; EXOME; GENETICS; HIGH THROUGHPUT SEQUENCING; HUMAN GENOME; PROCEDURES; SOFTWARE;

ALGORITHMS; AREA UNDER CURVE; COMPUTATIONAL BIOLOGY; DISEASE; EXOME; GENE FREQUENCY; GENOME, HUMAN; HIGH-THROUGHPUT NUCLEOTIDE SEQUENCING; HUMANS; MACHINE LEARNING; POLYMORPHISM, SINGLE NUCLEOTIDE; SOFTWARE;

EID: 85054427804     PISSN: 00029297     EISSN: 15376605     Source Type: Journal    
DOI: 10.1016/j.ajhg.2018.08.005     Document Type: Article

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