tealackingAI and Robotics

Nov 8, 2013 (3 years and 9 months ago)



This course aims at introducing bioengineers in the field of bioinformatics. The field of
bioinformatics is very broad and encompasses a wide range of research topics: sequence
analysis, data analysis of vast numbers of experimental data (high through
put data),
database management etc. In a set of theoretical courses the statistical insights and
principles underlying the bioinformatical methods will be explained and illustrated by
describing a selected set of tools into detail.

An overview of the diff
erent tools currently available, with clear reference to the
underlying statistical principles will be given.

Based on these theoretical insights, the bio
engineering should be able to understand and
make use of new tools. The application of the theory by

analysing the principles of a new
tool, will be part of the course (theoretical analysis of the tool, practical use on an dataset,
evaluation of the result).

This course is a good introduction to more specialised courses in genome analysis,
molecular mode
l building, drug design, etc.


Combinatorial methods:


backtracking, tree searching, heuristic methods


sequence alignment (Needlemann Wunsh, Smith Waterman)

Blast, PSI

Statistical Methods:


ariate statistics

Bayesian statistics

HMM (hidden markov models)

NN (neural networks)

EM (expectation maximization)

Optimization techniques

Examples: DNA and proteins

Repeats and CpG islands (HMM)

Analyzing high throughput data (Clustering)

ylogenetic analysis: general introduction and overview of methods


Architecture, types (relational, object
oriented and mixture models), database
administration systems, introduction to SQL, problems applied to biological databases,

querying (overview of the distinct databases on the internet).


1. choice from one of the following topics

1 phylogenetic analysis (substitution model building, tree building and evaluation)

2 gene prediction, promoter prediction, predicti
on intron
exon boundaries

3. retrieval of protein domains, families (HMM)

4. retrieval of motifs (protein, DNA) (HMM, EM)

5. prediction of secondary protein structures (NN, HMM)

6. analysis of ligand binding sites and protein
protein interface (in sili
co 2 hybrid)

7. classification (NN, PCA, SVM)

8. genetic network inference (Bayesian networks)

9. querying databases

2. Use of an integrated web tool with the emphasis on the importance of linking distinct
algorithms to obtain biologically relevant con

In Silico exercises

Teaching activities:

Lectures and exercises on PC


Bioinformatics. A practical guide to the analysis of genes and proteins. Baxevanis A.D. and Ouelllete
B.F.F. (1998), John Wiley & Sons, Inc., New York.

sequence analysis. Durbin, R., Eddy, S., Krogh, A., Mitchinson, G. (1998), Cambridge
University press.

Bioinformatics: the machine learning approach. Brunak, S. 1998, MIT Press.