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Activity Number:
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506
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #308159 |
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Title:
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Using Information Theory for the Classification of Trends in Dose-Response Microarray Experiments
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Author(s):
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Luc Bijnens*+ and Dan Lin and Ziv Shkedy and An De Bondt and Ilse Van Den Wyngaert and Tamara Geerts and Timothy Perera and Hinrich Goehlmann
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Companies:
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Johnson & Johnson PRD and Hasselt University and Hasselt University and Janssen Pharmaceutica - JJPRD and Janssen Pharmaceutica - JJPRD and Janssen Pharmaceutica - JJPRD and Janssen Pharmaceutica - JJPRD and Johnson & Johnson PRD
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Address:
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Turnhoutseweg 30, Beerse, 2340, Belgium
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Keywords:
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microarrays ; monotonic regression ; information theory ; minimum effective dose ; dose-responses
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Abstract:
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Microarray experiments to investigate dose-responses consist of correlating expression levels of thousands of genes with several doses of treatments. Recently we developed several testing procedures to test for monotonic trends based on isotonic regressions of the observed means by using the methods of Barlow et al.(1972). Once a monotonic relationship between the gene expression and dose is established, there is a set of several possible monotone models which can be fitted to the data. A selection of the best model from this set allows us to identify both the shape of dose-response curve and the minimum effective dose level. The posterior probability of the model and the evidence ratio are calculated using information criteria which take into account both the goodness-of-fit and the complexity of the models. The method is applied to an experiment with 12 samples measuring 16998 genes.
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