Bayesian model selection for mining mass spectrometry data

Neural Networks

Volumn 18, Issue 5-6, 2005, Pages 843-849

  Saksena, Anshu ^a   Lucarelli, Dennis ^a   Wang, I Jeng ^a  

^a JOHNS HOPKINS UNIVERSITY   (United States)

Author keywords

Bayesian networks; Biomarker; Domain knowledge; Mass spectrometry; Microorganism detection; Model selection; Structure learning

Indexed keywords

COMPUTATIONAL COMPLEXITY; MASS SPECTROMETRY; MATHEMATICAL MODELS; MICROORGANISMS; SPURIOUS SIGNAL NOISE;

BAYESIAN NETWORKS; DOMAIN KNOWLEDGE; MICROORGANISM SELECTION; MODEL SELECTION; STRUCTURE LEARNING;

DATA MINING;

ALGORITHM; BACILLUS SUBTILIS; BAYES THEOREM; CONFERENCE PAPER; LEARNING; MASS SPECTROMETRY; MATHEMATICAL MODEL; MICROORGANISM DETECTION; NONHUMAN; PRIORITY JOURNAL; TIME OF FLIGHT MASS SPECTROMETRY; ARTICLE; ARTIFICIAL INTELLIGENCE; FACTUAL DATABASE; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DATABASES, FACTUAL; MASS SPECTROMETRY; MODELS, STATISTICAL; SPECTROMETRY, MASS, MATRIX-ASSISTED LASER DESORPTION-IONIZATION;

EID: 27744558757     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2005.06.046     Document Type: Conference Paper

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