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.
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Pristupačnost
