Tik-61.181 Informaatiotekniikan erikoiskurssi I (4 ov) (L)

Prof. Sami Kaski, Prof. Heikki Mannila, MSc Janne Nikkilä
Semester: Autumn 2000
Credit points: 4 cr
Place: Seminar room U261 in the main building of HUT
Time: Tuesdays 14-16, starting from September 19
Language: English (or Finnish)
Homepage: http://www.cis.hut.fi/Opinnot/Tik-61.181/

Bioinformatics

Bioinformatics is about the creation and development of advanced information and computational technologies for problems in biology. Special emphasis is usually on computational molecular biology, especially analyzing the genome. Advanced methods are needed for analyzing the vast amounts of data produced by the human genome project and data of other species - for extracting genes and especially for understanding the functions of the genes.

The purpose of the course is to learn about the methods that are being used in bioinformatics. Biological background will be treated to the extent that is necessary.

Tentative application areas include genomic sequence analysis, protein structure prediction, phylogenetic trees, and analysis of gene expression data obtained from DNA chips. The methods will include pattern discovery methods used in data mining, probabilistic models, and models of computational intelligence.

The course will be concluded in a symposium on bioinformatics, in which invited speakers will talk about their research. Tentative speakers include Prof. Juha Kere of the Finnish Genome Center and Prof. Eero Castren of the A.I. Virtanen Institute for Molecular Sciences.

The course is based on the following material:

1. J. Setubal and J. Meidanis: Introduction to Computational Molecular Biology, Brooks/Cole Publishing Company 1997. Available at least in electronic bookstores.

2. S.L. Salzberg, D.B. Searls and S. Kasif: Computational Methods in Molecular Biology, Elsevier 1999. Available at least in electronic bookstores.

3. R. Durbin, S. Eddy, A. Krogh and G. Mitchison: Biological Sequence Analysis, Probabilistic models of proteins and nucleic acids, Cambridge University Press. Available at least in electronic bookstores.

4. P. Baldi and S. Brunak: Bioinformatics, The machine learning approach, MIT Press 1998.

5. Research articles:

Expression profiling using cDNA microarrays (Duggan et al., Nature gen. suppl., vol 21, Jan 1999)

Gene expression data analysis (Brazma & Vilo, FEBS Letters 480, 2000,p.17-24)

A post-genomic challenge: learning to read patterns of protein synthesis (Alison Abbot,news briefing, Nature, vol. 402, 1999)

Clustering gene expression patterns (Ben-Dor, ACM recomb 1999, pp.33-42)

Interpreting patterns of gene expression with self-organizing maps: Methods and application to hetopoietic differentiation (Proc.Natl.Acad.Sci.USA, vol96,pp.2907-2912,1999)

Cluster analysis and display of genome-wide expression patterns (Eisen et al. PNAS USA, vol95, pp. 14863-14868,1998)

Knowledge-based analysis of microarray gene expression data by using support vectro machines (Brown et al. ,PNAS 2000, vol 97.,pp.262-267)

Singular value decomposition for genome-wide expression data processing and modeling (Alter et al., PNAS 2000, vol.97,pp.10101-10106)

Identifying gene regulatory networks from experimental data (Chen et al., ACM RECOMB 1999, pp.94-103)

Predicting gene regulatory elements in silico on a genomic scale (Brazma et al., Genome, vol 8, pp.1202-1215, 1998)

To pass the course (4cr), you have to

1. participate actively,
2. give a seminar talk on a part of the books or some papers,
3. prepare a brief summary handout of the material, for example in the form of copies of your clear transparencies, and
4. solve a set of exercises given during the seminar
5. do a small project work, a small-scale research project.

More information

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