High-dimensional biomarkers in personalized medicine via variable importance ranking
View Presentation Hongshik Ahn, Stony Brook University James J Chen, NCTR - FDA Ralph L Kodell, University of Arkansas for Medical Sciences *Hojin Moon, California State University - Long Beach Keywords: Cross-Validation, Ensemble, Feature selection We present robust classification algorithms for high-dimensional genomic data to find biomarkers, by analyzing variable importance, that enable a better diagnosis of disease, an earlier intervention, or a more effective assignment of therapies. The goal is to isolate a set of genes which play an important role in classification with respect to the prognosis or types of diseases in order to maximize efficacy or minimize toxicity in personalized treatment of a life-threatening disease using several methods for variable importance ranking. First, we present a highly accurate classification algorithm with a method for selecting highly ranked variables as a high-dimensional biomarker. We assess the prediction accuracy of the biomarker by cross-validation using published high-dimensional data sets. This approach can discover genomic biomarkers underlying both adverse and efficacious outcomes.
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Key Dates
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April 30 - May 22, 2013
Invited Abstract Submission Open -
June 4, 2013
Online Registration Opens -
August 9 - August 23, 2013
Invited Abstract Editing -
August 23, 2013
Short Course materials due from Instructors -
August 26, 2013
Housing Deadline -
September 9, 2013
Cancellation Deadline and Registration Closes @ 11:59 pm EDT -
September 16 - September 18, 2013
Marriott Wardman Park, Washington, DC