A hierarchical Naïve Bayes model for handling sample heterogeneity in classification problems: An application to tissue microarrays

BMC Bioinformatics

Volumn 7, Issue , 2006, Pages

  Demichelis, Francesca ^a,b,c   Magni, Paolo ^d   Piergiorgi, Paolo ^d   Rubin, Mark A ^b,c,e   Bellazzi, Riccardo ^d  

^a UNIVERSITY OF TRENTO   (Italy)

^b BRIGHAM AND WOMEN S HOSPITAL   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

^d UNIVERSITY OF PAVIA   (Italy)

^e DANA FARBER HARVARD CANCER CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN HIERARCHICAL MODEL; CLASSIFICATION ACCURACY; CLASSIFICATION EQUATIONS; CLASSIFICATION MODELS; COMPUTATIONAL COSTS; MULTIPLE MEASUREMENTS; PROBABILISTIC FRAMEWORK; PROSTATE CANCER DATASET;

CLASSIFICATION (OF INFORMATION); EXPERIMENTS; GENE EXPRESSION; HIERARCHICAL SYSTEMS; MICROARRAYS; MOLECULAR BIOLOGY; PROTEINS;

TISSUE;

2METHYLACYL COENZYME A RACEMASE; PROTEIN EZH2; TUMOR MARKER; PROTEOME;

ACCURACY; ARTICLE; BIOINFORMATICS; CLASSIFICATION; DECISION MAKING; HUMAN; INTERMETHOD COMPARISON; PROBABILITY; PROSTATE BIOPSY; PROSTATE CANCER; PROTEIN POLYMORPHISM; SENSITIVITY AND SPECIFICITY; TISSUE MICROARRAY; VALIDATION PROCESS; ALGORITHM; ANIMAL; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; BIOLOGICAL MODEL; CONNECTIVE TISSUE; DNA MICROARRAY; GENE EXPRESSION PROFILING; METABOLISM; PROCEDURES; STATISTICAL ANALYSIS; STATISTICAL MODEL;

ALGORITHMS; ANIMALS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; CONNECTIVE TISSUE; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; HUMANS; MODELS, GENETIC; MODELS, STATISTICAL; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; PROTEOME;

EID: 33845660791     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-514     Document Type: Article

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