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Activity Number:
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469
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #310144 |
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Title:
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Correlating Large-Scale Biological Data with Censored Survival Data
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Author(s):
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Karthik Devarajan*+
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Companies:
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Fox Chase Cancer Center
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Address:
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333 Cottman Avenue, Philadelphia, PA, 19111,
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Keywords:
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supervised learning ; high-throughput study ; partial least squares ; gene expression ; microarray ; censored survival data
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Abstract:
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The advent of high-throughput technologies such as microarrays has resulted in large amounts of biological data in the form of expression profiles of thousands of genes and proteins. In recent years, there has been a tremendous interest in linking gene and protein expression data with outcome variables using supervised learning methods. An important application lies in correlating such large-scale data with censored survival data where the gene expression profile of a patient is used to predict the survival probability. In this paper, we survey the literature in this area as well as propose methods that combine learning theoretic approaches with survival models for censored data. We illustrate our methods via real-life cancer microarray data as well as simulations.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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