Data Mining in Gene Expression Data: Identifying Differentially Expressed Genes and Discovering Significant Submatrix Patterns

PhD Thesis Proposal Defence


Title: "Data Mining in Gene Expression Data: Identifying Differentially 
Expressed Genes and Discovering Significant Submatrix Patterns"

by

Miss Qiong FANG


ABSTRACT:

With the massive amount of gene expression data being generated, efficient
data mining techniques are in great need to mine from the gene expression
data interesting results, which could be a good reference before complex
biological validations.

One important problem in the area of gene expression analysis is to
identify differentially expressed genes, sinc such genes, exhibiting
sufficiently different expression levels under distinct experimental
conditions, could be critical for tracing the development and progression
of a disease. While the identified differentially expressed genes vary
across different microarray
studies, we propose an efficient weighted rank aggregation method to
combine the results from multiple studies in order to identify more
"reliable" differentially expressed genes.

The other problem we study is to discover submatrix patterns, more
specifically, the Order-Preserving Submatrices (OPSM), from gene
expression matrix. The OPSM model is employed to reveal intresting
biological associations among genes and experimental conditions. While the
OPSM model is too strictive in practice, we consider possible forms of
noise existing in real data, and propose several relaxed OPSM models.
Efficient mining
methods have also been presented for mining different the relaxed OPSM
patterns. Experimental studies on real biological data show that our
relaxed OPSM models better capture the characteristics of noisy OPSM
patterns.

In this proposal, we report our current work on these two problems and
discuss several on-going plans as future work.


Date:                   Friday, 11 November 2011

Time:                   11:00am - 1:00pm

Venue:                  Room 3584
                         lifts 27/28

Committee Members:      Dr. Wilfred Ng (Supervisor)
                         Dr. Lei Chen (Chairperson)
 			Prof. Dik-Lun Lee
 			Dr. Raymond Wong


**** ALL are Welcome ****