A new disease-specific machine learning approach for the prediction of cancer-causing missense variants

Genomics

Volumn 98, Issue 4, 2011, Pages 310-317

  Capriotti, Emidio ^a,c   Altman, Russ B ^a,b  

^a Stanford University   (United States)

^b STANFORD UNIVERSITY   (United States)

^c UNIVERSITAT DE LES ILLES BALEARS   (Spain)

Author keywords

Cancer causing variants; Gene Ontology; Machine learning; Single Nucleotide Polymorphisms; Support Vector Machine

Indexed keywords

ACCURACY; ARTICLE; CANCER CAUSING MISSENSE VARIANT; CLASSIFICATION ALGORITHM; CLASSIFIER; CORRELATION COEFFICIENT; GENETIC VARIABILITY; INTERMETHOD COMPARISON; PREDICTION; PRIORITY JOURNAL; SINGLE NUCLEOTIDE POLYMORPHISM; SUPPORT VECTOR MACHINE;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; GENETIC PREDISPOSITION TO DISEASE; GENOME, HUMAN; HUMANS; MUTATION, MISSENSE; NEOPLASMS; POLYMORPHISM, SINGLE NUCLEOTIDE; PREDICTIVE VALUE OF TESTS; SUPPORT VECTOR MACHINES;

EID: 80053297561     PISSN: 08887543     EISSN: 10898646     Source Type: Journal    
DOI: 10.1016/j.ygeno.2011.06.010     Document Type: Article

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