Probe-level linear model fitting and mixture modeling results in high accuracy detection of differential gene expression

BMC Bioinformatics

Volumn 7, Issue , 2006, Pages

  Lemieux, Sébastien ^a  

^a INSTITUTE FOR RESEARCH IN IMMUNOLOGY AND CANCER   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIAL GENE EXPRESSIONS; DIFFERENTIALLY EXPRESSED GENE; EXPRESSION LEVELS; FALSE POSITIVE; FINITE MIXTURES; IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES; LINEAR MODELING; TREATMENT EFFECTS;

GENE EXPRESSION; MIXTURES; PROBES;

MATHEMATICAL MODELS;

ACCURACY; ARTICLE; CONTROLLED STUDY; DNA MICROARRAY; EXPERIMENTAL DESIGN; FALSE POSITIVE RESULT; GENE EXPRESSION; GENE IDENTIFICATION; GENETIC ANALYSIS; LINEAR SYSTEM; MOLECULAR PROBE; NORMAL DISTRIBUTION; STATISTICAL MODEL; ALGORITHM; BIOLOGICAL MODEL; CLUSTER ANALYSIS; GENE EXPRESSION PROFILING; METHODOLOGY; OLIGONUCLEOTIDE PROBE; REPRODUCIBILITY; ROC CURVE; STATISTICAL ANALYSIS;

MESSENGER RNA;

ALGORITHMS; CLUSTER ANALYSIS; DATA INTERPRETATION, STATISTICAL; GENE EXPRESSION PROFILING; LINEAR MODELS; MODELS, GENETIC; NORMAL DISTRIBUTION; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; OLIGONUCLEOTIDE PROBES; REPRODUCIBILITY OF RESULTS; RNA, MESSENGER; ROC CURVE;

EID: 33749326335     PISSN: 14712105     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-7-391     Document Type: Article

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