Quality Assessment of Affymetrix GeneChip Data using the EM Algorithm and a Naive Bayes Classifier

Recent research has demonstrated the utility of using supervised classification systems for automatic identification of low quality microarray data. However, this approach requires annotation of a large training set by a qualified expert. In this paper we demonstrate the utility of an unsupervised c...

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Bibliographic Details
Published in2007 IEEE 7th International Symposium on BioInformatics and BioEngineering Vol. 7; pp. 145 - 150
Main Authors Howard, B.E., Sick, B., Perera, I., Yang Ju Im, Winter-Sederoff, H., Heber, S.
Format Conference Proceeding Journal Article
LanguageEnglish
Published IEEE 01.01.2007
Subjects
Algorithm design and analysis
Bioinformatics
Context modeling
Data analysis
EM algorithm
microarray
Naïve Bayes
Plants (biology)
Quality assessment
Quality control
Systems biology
Testing
Training data
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Summary:Recent research has demonstrated the utility of using supervised classification systems for automatic identification of low quality microarray data. However, this approach requires annotation of a large training set by a qualified expert. In this paper we demonstrate the utility of an unsupervised classification technique based on the Expectation-Maximization (EM) algorithm and naive Bayes classification. On our test set, this system exhibits performance comparable to that of an analogous supervised learner constructed from the same training data.
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ISBN:1424415098
9781424415090
DOI:10.1109/BIBE.2007.4375557