Bioinformatics 2

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Bioinformatics 2

Code: 239987
ECTS: 6.0
Lecturers in charge: doc. dr. sc. Pavle Goldstein
Take exam: Studomat
Load:

1. komponenta

Lecture typeTotal
Lectures 30
Exercises 30
* Load is given in academic hour (1 academic hour = 45 minutes)
Description:
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.
Literature:
  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.
Prerequisit for:
Enrollment :
Attended : Bioinformatics 1

Examination :
Passed : Bioinformatics 1
2. semester
Mandatory course - Regular study - Biomedical Mathematics
Consultations schedule: