1. komponenta

Lecture type	Total
Lectures	30
Exercises	30

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE OBJECTIVES:
1. modelling evolution of biological sequences - multiple sequence alignment; hidden Markov models for multiple sequence alignment and associated topics,
2. protein structure analysis: protein fold, secondary structure elements, distance matrices; structure alignment,
3. phylogenetic analysis - UPGMA and neighbour-joining algorithms
4. clustering and classification: k-means clustering, how many k in k-means; elements of machine learning for biological sequence classification - support vector machines.

COURSE CONTENT:
1. modelling evolution of biological sequences - multiple sequence alignment; hidden Markov models for multiple sequence alignment and associated topics,
2. protein structure analysis: protein fold, secondary structure elements, distance matrices; structure alignment,
3. phylogenetic analysis - UPGMA and neighbour-joining algorithms,
4. clustering and classification: k-means clustering, how many k in k-means; elements of machine learning for biological sequence classification - support vector machines.

1. Biological Sequences Analysis, Durbin et al, CUP.
2. Statistical Methods in Bioinformatics, An Introduction, Ewens, W.J., Grant, G.R., Springer, 2001.
3. Cross-Validatory Choice and Assessment of Statistical Predictions, M. Stone, Journal of the Royal Statistical Society. Series B (Methodological),Vol. 36, No. 2 (1974).
4. Weighing the Odds, A Course in Probability and Statistics, Williams, David, Cambridge UP, 2001.

Enrollment :
Attended : Bioinformatics 1

Examination :
Passed : Bioinformatics 1

Mandatory course - Regular study - Biomedical Mathematics