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A substantial core of computational biology (or bioinformatics) methods has been developed during the past three decades to meet the need of biological scientists for data storage, data retrieval, and data analysis. The databases of DNA and protein sequences contain millions of sequences, many completed genomes, and more are coming rapidly. DNA microarray data are being produced at a phenomenal speed. Protein arrays are being developed. High throughput structural data are being produced. Analysis of these data using bioinformatics tools has played a key role in several recent advances and will play increasingly important roles in future biomedical researches.
A main problem that motivated early research in computational biology is protein sequence analysis. Recently, because of the dramatic increase in many types of biological data due to the human genome project and other high-throughput projects, the scope of bioinformatics research has been extended to embrace diverse topics such as microarray analysis, protein classification, regulatory motif analysis, RNA analysis etc.
The sheer amount and variety of the molecular biology data have already presented a major challenge to all quantitative researchers. A distinctive feature of these data, be they microarray images, DNA sequences or protein structures, is that there is a large body of biological knowledge associated with them. This makes standard data mining or statistical analysis tools less effective. Incorporating relevant scientific knowledge into the development of statistical or computational analysis tools is the key to success.
This short course is intended to provide coverage of some key developments of bioinformatics in the past thirty years with an emphasis on topics of recent interest. Topics include: pair-wise sequence analysis, local alignment, dynamic programming, BLAST, multiple sequence alignment, Gibbs motif sampler, gene regulation, hidden Markov models, context-free grammars, protein structure analysis, comparative genomics, model-based microarray analysis, clustering methods for microarrays, phylogenetic trees, etc.
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= Applied Session,
= Theme Session,
= Presenter