This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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582
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Type:
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Contributed
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Date/Time:
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Wednesday, August 4, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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Abstract - #308276 |
Title:
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Predicting Patient Survival from Proteomic Profile Using MALDI-TOF Mass Spectrometry Data
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Author(s):
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Farida Mostajabi*+ and Susmita Datta
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Companies:
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University of Louisville and University of Louisville
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Address:
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8565 daly rd Apt#7, Cincinnati, OH, 45231, U.S.A
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Keywords:
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MALDI-TOF ;
LASSO ;
PLS ;
regularized and penalized methods
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Abstract:
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In this project we consider predicting survival time from the proteomic profiles of patient serum peptide using Matrix-assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) data of non-small cell lung cancer (NSCLC) patients. Due to large dimension of features in a mass spectrum, traditional linear regression modeling failure times with high number of proteomic features is not feasible. We address this problem by fitting linear regression model of log-transformed failure times using regularized and penalized methods such as least absolute shrinkage and selection operator(LASSO), partial least squares (PLS), elastic net regularization and sparse partial least square(SPLS). In this work, we study the performances of all these models in terms of fit to the patient survival time and prediction of the future survival times using leave-out-one cross validation technique.
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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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