DNA Microarray Data Clustering and Biclustering

Speaker:	Prof. Hong Yan
		Department of Electronic Engineering
		City University of Hong Kong

Title:		"DNA Microarray Data Clustering and Biclustering"

Date:		Monday, 6 February 2006

Time:		4:00pm - 5:00pm

Venue:		Lecture Theatre F
		(Leung Yat Sing Lecture Theatre, near lift nos. 25/26)
		HKUST

ABSTRACT:

The DNA microarray allows the measurement of expression levels of
thousands of genes simultaneously. Microarray data analysis is a
challenging problem and has attracted enormous interests from researchers
in science and engineering. Microarray data can be represented as a
matrix, in which rows correspond to genes and columns to conditions or
time points. In clustering, we perform classification along either the row
or the column direction, while in biclustering we perform classification
along both row and column directions. In this seminar, I shall present our
recent work on microarray data clustering and biclustering. We have
develop a competitive learning based method to find natural clusters in
the data and use partial knowledge as constraints to improve the stability
of the clustering results. For time-series data, we have developed an
autoregressive model based technique to analyse the spectral similarity
between gene expression profiles. This method has identified gene
regulations at different frequencies, in a way similar to frequency
division in communication systems. Biclustering is often associated with
the stigma of being inherently intractable in general because of its
computational complexity. We have recently developed a hyperplane model
for solving this problem. By separating different types of biclusters and
filtering out irrelevant data, our method can reduce the computational
complexity substantially and extract large biclusters. The method has been
applied to medical diagnosis using microarray data.


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Biography:

Hong Yan received his Ph.D. degree from Yale University. He has been
Professor of Imaging Science at the University of Sydney and currently is
Professor of Computer Engineering at City University of Hong Kong. His
research interests include image processing, pattern recognition and
bioinformatics. Professor Yan is elected a Fellow of the Institute of
Electrical and Electronic Engineers (IEEE) for contributions to image
recognition techniques and applications and a Fellow of the International
Association for Pattern Recognition (IAPR) for contributions to document
image analysis.  He is also a Fellow of the Institution of Engineers,
Australia (IEAust) and a member of the International Society for
Computational Biology (ISCB).