Bayesian neural networks for bivariate binary data: An application to prostate cancer study

Statistics in Medicine

Volumn 24, Issue 23, 2005, Pages 3645-3662

  Chakraborty, Sounak ^a   Ghosh, Malay ^a   Maiti, Tapabrata ^b   Tewari, Ashutosh ^c  

^a University of Florida ^*  (United States)

^b Department of Statistics Center for Statistics and Applications in Forensic Evidence Iowa State University 2438 Osborn Dr Ames   (United States)

^c NEW YORK PRESBYTERIAN HOSPITAL   (United States)

Author keywords

Bayesian prediction; Bivariate logistics regression; Feedforward neural network; Gene expression microarrays; Margin positivity; Markov chain Monte Carlo; Seminal vesicle positivity

Indexed keywords

PROSTATE SPECIFIC ANTIGEN;

ACCURACY; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; CANCER LOCALIZATION; CANCER RADIOTHERAPY; CANCER RECURRENCE; CLINICAL FEATURE; COVARIANCE; DATA ANALYSIS; DNA MICROARRAY; GENE EXPRESSION PROFILING; GLEASON SCORE; HUMAN; INTERMETHOD COMPARISON; MALE; MATHEMATICAL ANALYSIS; METASTASIS; MONTE CARLO METHOD; OUTCOMES RESEARCH; POSTOPERATIVE PERIOD; PREDICTION; PREOPERATIVE EVALUATION; PROBABILITY; PROSTATE BIOPSY; PROSTATE CANCER; PROSTATE SURGERY; SEMINAL VESICLE; SIMULATION; STATISTICAL MODEL; UNITED STATES; VALIDATION PROCESS;

BAYES THEOREM; BIOMETRY; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; HUMANS; LOGISTIC MODELS; MALE; MARKOV CHAINS; MODELS, STATISTICAL; MONTE CARLO METHOD; NEURAL NETWORKS (COMPUTER); OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PROSTATIC NEOPLASMS;

EID: 28244435110     PISSN: 02776715     EISSN: None     Source Type: Journal    
DOI: 10.1002/sim.2214     Document Type: Article

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