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Bayesian automatic relevance determination algorithms for classifying gene expression data.

Yi Li·Colin Campbell·Michael Tipping
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Abstract

MOTIVATION: We investigate two new Bayesian classification algorithms incorporating feature selection. These algorithms are applied to the classification of gene expression data derived from cDNA microarrays. RESULTS: We demonstrate the effectiveness of the algorithms on three gene expression datasets for cancer, showing they compare well with alternative kernel-based techniques. By automatically incorporating feature selection, accurate classifiers can be constructed utilizing very few features and with minimal hand-tuning. We argue that the feature selection is meaningful and some of the highlighted genes appear to be medically important.

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AlgorithmsBayes TheoremCluster AnalysisColonic NeoplasmsDatabases, Nucleic AcidFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGenetic MarkersHumansLeukemia, Myeloid, AcuteModels, GeneticNeoplasmsOligonucleotide Array Sequence AnalysisOvarian NeoplasmsPattern Recognition, AutomatedPrecursor Cell Lymphoblastic Leukemia-LymphomaQuality ControlReproducibility of ResultsSensitivity and SpecificitySequence Analysis, DNAComparative StudyEvaluation StudyJournal ArticleValidation Study

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Yi Li, Colin Campbell, Michael Tipping. 2002. Bayesian automatic relevance determination algorithms for classifying gene expression data.. https://doi.org/10.1093/bioinformatics%2F18.10.1332

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