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Abstract Details
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Activity Number:
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600
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Type:
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Invited
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #300362 |
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Title:
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Order Statistics, Marker Interactions, and Molecular Medicine
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Author(s):
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Donald Geman*+
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Companies:
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The Johns Hopkins University
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Address:
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302A Clark Hall, Baltimore, MD, 21218,
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Keywords:
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Molecular medicine ;
Cancer ;
Biomarkers ;
Order statistics ;
Statistical learning
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Abstract:
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The goal of translational medicine is to carry fundamental research into clinical practice. I will talk about several such projects in computational genomics, including cancer biomarker discovery and genetic network regulation, where the underlying methodology is statistical learning based on high-dimensional, high-throughput molecular data. Perhaps the main barrier to mature applications is the "black box" decision rules generated by standard methods in computational learning, which severely impede biomedical understanding. I will argue that low-dimensional order statistics are easy to interpret and can account for combinatorial interactions among genes and gene products, supplying statistical arguments about bias and variance as well as evidence through a growing number of validation studies in predicting cancer phenotypes.
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Authors who are presenting talks have a * after their name.
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