Plenary
Lecture
Efficient Biclustering Algorithms and Their Applications
to Multidimensional Biological Data Analysis
Professor Hong Yan
Department of Electronic Engineering
City University of Hong Kong
Kowloon, Hong Kong
E-mail:
h.yan@cityu.edu.hk
Abstract: Clustering algorithms have many useful
applications in signal processing and pattern
recognition. In conventional clustering methods, we
classify objects in terms of all features available and
measure the similarity between two objects using a
distance metric. In biclustering, we detect coherent
patterns in both object and feature directions and we
are interested in data consistency, such as simultaneous
downward or upward changes of a subset of features for a
subset of objects, rather than the distances among all
objects for all features. Biclustering is naturally more
difficult than clustering computationally and is often
considered intractable mathematically. We have recently
developed hyperplane based methods for the detection of
a class of biclusters in a high-dimensional signal
space. Our methods provide a unified model for several
types of biclusters and can be implemented using
efficient signal processing algorithms. We have also
found an interesting link between biclustering and the
spectral graph theory. We have applied our biclustering
methods to disease diagnosis and drug therapeutic effect
assessment using DNA microarray gene expression data.
For example, we are able to identify biclusters of a
subset of genes that co-express under a subset of
conditions. These biclusters are useful for the
identification of caner types and sub-types. We have
also implemented biclustering algorithms on the
field-programmable gate array (FPGA) for fast
computation. In this seminar, the recent work of our
research group on biclustering methods and their
applications will be presented.
Brief Biography of the Speaker:
Hong Yan received his Ph.D. degree from Yale University.
He was Professor of Imaging Science at the University of
Sydney and is currently Professor of Computer
Engineering at City University of Hong Kong. His
research interests include image processing, pattern
recognition and bioinformatics, and he has over 300
journal and conference publications in these areas.
Professor Yan was elected an IAPR fellow for
contributions to document image analysis and an IEEE
fellow for contributions to image recognition techniques
and applications.
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