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UNIVERSITI TEKNOLOGI MALAYSIA
DECLARATION OF THESIS / UNDERGRADUATE PROJECT PAPER AND COPYRIGHT
Author's full name : CHOON YEE WEN
Date of birth
:
11.04.1986
Title
INFORMATIVE GENE SELECTION USING BAYESIAN MODEL
AVERAGING FOR PATIENTS SURVIVAL ANALYSIS
Academic Session: 2009/2010
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SIGNATURE
SIGNATURE OF SUPERVISOR
860411 23 6684
Mr. Afnizanfaizal Bin Abdullah
-
-
(NEW IC NO. /PASSPORT NO.)
Date 11 APRIL 2010
NOTES :
NAME OF SUPERVISOR
Date : 11 APRIL 2010
If the thesis is CONFIDENTIAL or RESTRICTED, please attach with the letter from
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iii
INFORMATIVE GENE SELECTION USING BAYESIAN MODEL AVERAGING
FOR PATIENTS SURVIVAL ANALYSIS
CHOON YEE WEN
This thesis is submitted in partial fulfillment of the requirements for the award of the
Bachelor of Computer Science Degree in Bioinformatics
Faculty of Computer Science and Information Systems
Universiti Teknologi Malaysia
April 2010
VI
ABSTRACT
Microarray technology is now widely used to identify potential biomarkers for
cancer prognostics and diagnostics. With a vast number of genes information
produced by microarray, informative gene selection is needed to both decrease
clinical costs and mitigates the possibility of overfitting due to high inter variable correlations. A problem with feature selection algorithms used to
produce continuous predictors of patient survival is that they fail to explain the
model (a set of selected genes whose regression coefficients haven been
calculated for use in predicting survival prognosis) uncertainty. With thousands
of genes and only tens to hundreds of samples, it often happens that a number
of different models describe the data about equally well. In this research.
BMA (Bayesian Model Averaging) method is applied to select a subset of
genes for survival analysis on microarray data. BMA combines the
effectiveness of multiple models by taking the weighted average posterior
distribution instead of choosing a single model and proceeding as if the data
were generated from it. In this research, BMA method showed that it can
successfully select the related genes and produce significant result for the
experiments in shorter time. The results obtained from BMA method proved
that BMA is an appropriate method to use in gene selection.
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