Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data

BMC Bioinformatics

Volumn 8, Issue , 2007, Pages

  Zhao, Xin ^a   Cheung, Leo Wang Kit ^b  

^a University of Hawai'i at Manoa   (United States)

^b LOYOLA UNIVERSITY MEDICAL CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MACHINE LEARNING APPROACHES; MACHINE LEARNING METHODS; MICROARRAY DATA ANALYSIS; MICROARRAY GENE EXPRESSION DATA; MICROARRAY GENE EXPRESSION DATA ANALYSIS; MODEL SELECTION PROBLEM; PROBABILISTIC PREDICTION; STATE-OF-THE-ART METHODS;

ADAPTIVE ALGORITHMS; BAYESIAN NETWORKS; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); GENE EXPRESSION; LEARNING SYSTEMS; MATHEMATICAL MODELS;

COMPUTER SIMULATION;

ACUTE LEUKEMIA; ALGORITHM; ANALYTICAL PARAMETERS; ARTICLE; BAYES THEOREM; BREAST CANCER; COLON TUMOR; CONTROLLED STUDY; DISEASE CLASSIFICATION; GENE EXPRESSION PROFILING; GENE IDENTIFICATION; HUMAN; INTERMETHOD COMPARISON; KERNEL METHOD; MACHINE LEARNING; MATHEMATICAL MODEL; MICROARRAY ANALYSIS; NUCLEOTIDE SEQUENCE; PREDICTION; PROBABILITY; PROCESS DEVELOPMENT; PROCESS MODEL; REGRESSION ANALYSIS; SAMPLER; SIMULATION; SMALL CELL SARCOMA; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; DNA MICROARRAY; METABOLISM; NEOPLASMS; NORMAL DISTRIBUTION; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL MODEL;

TUMOR MARKER; TUMOR PROTEIN;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYES THEOREM; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; GENE EXPRESSION PROFILING; MODELS, BIOLOGICAL; MODELS, STATISTICAL; NEOPLASM PROTEINS; NEOPLASMS; NORMAL DISTRIBUTION; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; TUMOR MARKERS, BIOLOGICAL;

EID: 33947380080     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-8-67     Document Type: Article

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