Next Generation Marketing Inc. Pleasanton, CA Sep 2014 to Oct 2014 Marketing CoordinatorCVS Pharmacy Sunnyvale, CA Sep 2011 to Aug 2012 Pharmacy ClerkSanta Clara High School Santa Clara, CA Jan 2011 to May 2011 Assistant Badminton CoachBintang Badminton Academy Sunnyvale, CA Oct 2008 to Oct 2009 Cashier and Salesman
Education:
University of California Davis, CA 2012 to 2014 Bachelor of Science in Managerial Economics
Skills:
Proficient with Microsoft Office [] Strong analytic skills, Ability to multi-task efficiently, Very organized and punctual, Motivated professional with effective problem-solving abilities, Basic knowledge of HTML, CSS, and JavaScript.
St Jude Radiology Medical Group Inc 101 E Valencia Mesa Dr, Fullerton, CA 92835 7149923000 (phone), 7149923928 (fax)
Education:
Medical School Tufts University School of Medicine Graduated: 1994
Languages:
English
Description:
Dr. Nguyen graduated from the Tufts University School of Medicine in 1994. He works in Fullerton, CA and specializes in Diagnostic Radiology. Dr. Nguyen is affiliated with Saint Jude Medical Center.
David Rocke - Davis CA, US Danh Nguyen - College Station TX, US
Assignee:
The Regents of the University of California
International Classification:
G06F019/00 G01N033/50
US Classification:
702/019000, 702/022000
Abstract:
Provided are methods of classifying biological samples based on high dimensional data obtained from the samples. The methods are especially useful for prediction of a class to which the sample belongs under circumstances in which the data are statistically under-determined. The advent of microarray technologies which provide the ability to measure en masse many different variables (such as gene expression) at once has resulted in the generation of high dimensional data sets, the analysis of which benefits from the methods of the present invention. High dimensional data is data in which the number of variables, p, exceeds the number of independent observations (e.g. samples), N, made. The invention relies on a dimension reduction step followed by a logistic determination step. The methods of the invention are applicable for binary (i.e. univariate) classification and multi-class (i.e. multivariate) classifications. Also provided are data selection techniques that can be used in accordance with the methods of the invention.
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