Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: A learning classifier system approach

Journal of the American Medical Informatics Association

Volumn 20, Issue 4, 2013, Pages 603-612

  Urbanowicz Dr , Ryan John ^a   Andrew, Angeline S ^a   Karagas, Margaret Rita ^a   Moore, Jason H ^a  

^a DARTMOUTH MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DNA;

ARTICLE; BLADDER CANCER; CANCER RECURRENCE; CANCER RISK; CANCER STAGING; CANCER SUSCEPTIBILITY; COMPARATIVE STUDY; EPISTASIS; EVOLUTIONARY ALGORITHM; GENETIC ASSOCIATION; GENETIC EPIDEMIOLOGY; GENETIC HETEROGENEITY; GENETIC RISK; HUMAN; OUTCOME ASSESSMENT; OUTCOME VARIABLE; SINGLE NUCLEOTIDE POLYMORPHISM; SMOKING; SURVIVAL TIME;

BLADDER CANCER; EPISTASIS; HETEROGENEITY; LEARNING CLASSIFIER SYSTEM; SMOKING; XPD;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CLASSIFICATION; COMPUTATIONAL BIOLOGY; EPISTASIS, GENETIC; GENETIC HETEROGENEITY; GENETIC PREDISPOSITION TO DISEASE; HUMANS; INDIVIDUALIZED MEDICINE; POLYMORPHISM, SINGLE NUCLEOTIDE; URINARY BLADDER NEOPLASMS;

EID: 84882384758     PISSN: 10675027     EISSN: 1527974X     Source Type: Journal    
DOI: 10.1136/amiajnl-2012-001574     Document Type: Article

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