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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