Transcript No Slide Title

COE Technology Week 2002 Focus Seminar
Organised by :
Recent Advances in Bioinformatics and Computational Biology
8 March, 2.00pm - 5.00pm
LT8, Level 2, North Spine
Introduction to BIRC Research
13:45
Registration
14:00
Introduction to BIRC Research
A/P Jagath C. Rajapakse
Deputy Director, BIRC
Nanyang Technological University
14:10
Some Sample Problems and
Solutions in Post-Genome
Knowledge Discovery
A/P Limsoon Wong
Institute for Infocomm Research
14:40
The Fugu Genome at the Verge of a
New Bioinformatics Explosion
Mr Elia Stupka
Fugu Informatics, IMCB
15:10
Refreshments
15:30
Getting Your Data-Driven Life
Sciences Research Up and Running
Mr Amey V. Laud
HeliXense Pte Ltd
16:00
Applications of Metaheuristics in
Bioinformatics
Dr Kuo-Bin Li
BioInformatics Institute
16:30
Multimodality as a Criterion for
Feature Selection in Unsupervised
Analysis on Gene Expression Data
Dr Li Yi
Genomics Institute of Singapore
A/P Jagath C. Rajapakse
Deputy Director, BIRC, Nanyang Technological University
Research at BIRC aims at the design and development of algorithms and
tools to store, analyze, and visualize biological data. Current research
projects are in structural and functional genomics, neuroinformatics and
medical informatics, data visualization, mining, and integration, and grid
computing. This talk will briefly outline some projects presnetly carried out
at BIRC
Some Sample Problems and Solutions in
Post-Genome Knowledge Discovery
A/P Limsoon Wong
Institute for Infocomm Research
Informatics has helped in launching molecular biology into the genomic era.
It appears certain that informatics will continue to be a major factor in the
success of molecular biology in the post-genome era. In this talk, we
describe advances made in data mining technologies that are relevant to
molecular biology and biomedical sciences. In particular, we discuss some
recent research results on topics such as (a) the prediction of immunogenic
peptides, (b) the discovery of gene structure features, (c) the classification of
gene expression profiles, and (d) the extraction of protein interaction
information from literature.
BioInformatics Research Centre
Applications of Metaheuristics in Bioinformatics
Dr Kuo-Bin Li
BioInformatics Institute
Many bioinformatics applications involve combinatorial search over a large
solution space. For example, multiple sequence alignment whose aim is to
find the optimal alignment of a group of nucleotide or protein sequences is a
combinatorial optimization problem. Metaheuristics are approaches that
guide local heuristic search procedure to explore the solution space beyond
local optimality. Examples of metaheuristics include genetic algorithm,
simulated annealing and tabu search. With the advent of powerful distributed
or parallel computers, new bioinformatics algorithms making use of
metaheuristics will hopefully be able to produce quality results within
reasonable amount of time. A few recent applications will be discussed.
Multimodality as a Criterion for
Feature Selection in Unsupervised Analysis on
Gene Expression Data
Dr Li Yi
Genomics Institute of Singapore
One important way that gene expression data is often analyzed is to
cluster the samples without reference to any annotation about them.
Before clustering, the data is often subjected to a feature selection
The Fugu Genome at the Verge of
a New Bioinformatics Explosion
Mr Elia Stupka
Fugu Informatics, IMCB
preprocessing step, in which a subset of genes is chosen for further
analysis. We examine the use of multimodality as a criterion for
choosing genes in feature selection, and compare its use with
variance, which is more commonly used at present. Both are compared
The completion of the Fugu genome marked an important event for
bioinformatics: the completion of the first of many vertebrate genomes
to be studied after the human genome was unveiled in 2001 which in
turn has opened the doors to comparative genomics. In this talk I will
discuss our work on the Fugu genome as well as on comparative genomics in
when used in conjunction with an algorithm that clusters the samples
in different ways, based on different subsets of the genes. The key
idea of this algorithm is to cluster genes using as a similarity measure
the mutual information between partitions on the samples obtained by
clustering the samples using the individual genes being compared.
the wider sense, the informatics challenges that it poses as well as the
17:00
End
biological discoveries it facilitates
Free Admission
All are Welcome