582606 Introduction to bioinformatics Autumn 2007 Esa Pitknen - - PowerPoint PPT Presentation
582606 Introduction to bioinformatics Autumn 2007 Esa Pitknen Master's Degree Programme in Bioinformatics (MBI) Department of Computer Science, University of Helsinki http://www.cs.helsinki.fi/mbi/courses/07-08/itb/ Introduction to
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− Course exam: maximum of 50 points − Exercises: maximum of 10 points
l 0% completed assignments gives you 0 points, 80% gives 10
l “A completed assignment” means that you are willing to
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− To get the lowest passing grade 1/5, you need to have at least 30
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− You need to have your Bachelor’s degree ready by August 2008
www.cs.helsinki.fi/mbi
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Department of Computer Science, Department of Mathematics and Statistics, HY Laboratory of Computer and Information Science, TKK Faculty of Medicine, HY Faculty of Biosciences, Faculty of Agriculture and Forestry, HY
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− Practical course in biodatabases (II period): techniques for
− Biological sequence analysis (III period): basic probabilistic
− Modeling of vision (III period): mechanisms and modeling of
− Seminar: Regulatory networks (I & II periods) − Seminar: Management of biological databases (III & IV
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− Statistical methods in genetic epidemiology and gene
− Mathematical modelling (I & II periods) − Practical course on phylogenetic analysis (IV period):
− Adaptive dynamics (III & IV periods)
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− Computational genomics (I & II periods): Algorithms and
− Signal processing in neuroinformatics (I and II periods):
− High-throughput bioinformatics (III and IV periods):
− Image analysis in neuroinformatics (III and IV periods):
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Computational biology
− Application of computing to biology (broad definition) − Often used interchangeably with bioinformatics
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Biometry: the statistical analysis of biological data
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Biophysics: "an interdisciplinary field which applies techniques from the physical sciences to understanding biological structure and function" -- British Biophysical Society
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Mathematical biology “tackles biological problems, but the methods it uses to tackle them need not be numerical and need not be implemented in software
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Overview of metabolic pathways in KEGG database, www.genome.jp/kegg/
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− Advanced data analysis methods are needed to make sense
− Typical data sources produce noisy data with a lot of missing
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Outi Monni, Biochip Center, Biomedicum
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