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

the wider sense, the informatics challenges that it poses as well as the

biological discoveries it facilitates

Introduction to BIRC ResearchA/P Jagath C. Rajapakse

Deputy Director, BIRC, Nanyang Technological University

Recent Advances in Bioinformatics and Computational Biology 8 March, 2.00pm - 5.00pm LT8, Level 2, North Spine

13:45 Registration 14:00 Introduction to BIRC Research

A/P Jagath C. Rajapakse

Deputy Director, BIRCNanyang 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 DataDr Li Yi

Genomics Institute of Singapore

 17:00 End

Free Admission All are Welcome

The Fugu Genome at the Verge of a New Bioinformatics Explosion

Mr Elia Stupka Fugu Informatics, IMCB

Organised by :

BioInformatics Research Centre

Applications of Metaheuristics in Bioinformatics Dr Kuo-Bin Li

BioInformatics Institute

COE Technology Week 2002 Focus SeminarCOE Technology Week 2002 Focus SeminarCOE Technology Week 2002 Focus SeminarCOE Technology Week 2002 Focus Seminar

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  

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.

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.

Multimodality as a Criterion forFeature Selection in Unsupervised Analysis on

Gene Expression DataDr 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

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

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.