Abstract Details
Activity Number:
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186
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Type:
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Contributed
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Date/Time:
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Monday, August 4, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #312424
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Title:
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Identifying Patient-Specific Biomarker and Predicting Anti-Cancer Drug Sensitivity via Robust Statistical Methodology
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Author(s):
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Heewon Park*+ and Teppei Shimamura and Seiya Imoto and Satoru Miyano
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Companies:
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University of Tokyo and University of Tokyo and University of Tokyo and University of Tokyo
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Keywords:
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Cancer biomarker ;
Drug sensitivity ;
Patient-specific analysis ;
Robust penalized regression
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Abstract:
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The patient-specific analysis enable to uncover individual genomic characteristics not averaged mechanisms for all patients, and thus we can effectively predict individual risk of disease and perform personalized anti-cancer therapy. Although the existing methods have been successfully uncovered crucial biomarkers, their procedures suffer from outliers, since the methods are based on non-robust manners. In practice, however, clinical and genomic alterations data contains outliers from various sources (e.g., experiment error, coding error, etc.), and outliers significantly disturb patient-specific analysis. We propose robust methodology for patient-specific analysis based on varying coefficient model and L1-type regularization. In the proposed method, outliers in high dimensional genomic data are controlled by Mahalanobis distance via principal component space. Thus, the proposed method effectively performs for patient-specific analysis without disturbance of outliers. We apply the proposed method to Sanger dataset from Cancer Genome Project for uncovering cancer biomarkers and predicting anti-cancer drug sensitivity, and show the effectiveness of our method.
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Authors who are presenting talks have a * after their name.
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