This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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524
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Learning and Data Mining
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Abstract - #309226 |
Title:
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Outcome-Informed Clustering of Gene Expression Profiles
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Author(s):
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Jessie Jann Hsu*+ and David Schoenfeld and Dianne Finkelstein
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Companies:
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Harvard University and Harvard University and Harvard University
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Address:
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170 Brookline Ave #1010, Boston, MA, 02215,
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Keywords:
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Clustering ;
K-means ;
Bayesian inference ;
Microarray
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Abstract:
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We present a model-based method for simultaneous clustering on gene expression data and outcome. Clustering is a popular learning technique used for subset and pattern discovery in high-dimensional data sets. Though model-based clustering of microarray data has been studied extensively, models that suggest a relationship between gene clusters and clinical outcomes have rarely been explored. We propose a joint model for clustering gene expression profiles and predicting patient outcome. We fit the model using a k-means clustering approach as well as a Bayesian clustering approach and compare the results through simulation studies. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program.
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