eduardo.eyras [at] upf.edu

- AGB1: Description of the course (Slides)
- AGB2: Probabilities (Slides)
(Maximum Likelihood Exercise)
(Bayes' Theorem Exercise)

- AGB3: The Classification Problem. Bayesian Classification
- AGB4: Naive Bayes classifier. (Slides)

- AGB5: Evaluating of models.
- AGB6: Accuracy measures, ROC curves
(Slides)

- AGB7: Information, Entropy. Entropy-based measures.
- AGB8: (Slides)
- AGB9: Feature selection. Information Gain. Decision trees
- AGB10:
**Sequence Motifs**

- AGB11: Description of known Motifs. Modelling dependencies. Markov models.
- AGB12: (Slides)
**Hidden Markov Models**

- AGB13: HMMs: Definition. Examples. (Slides)
- AGB14: The Viterbi, Forward and Posterior decoding algorithms.
Viterbi exercise: Donor model.
Viterbi exercise: Dishonest casino model.

- AGB15: HMM duration modeling.
- AGB16: Profile HMMs. (Slides)

- AGB17: Description and selection of assignments.
- AGB18:

- AGB19: Work in class
- AGB20:

- AGB21: Work in class
- AGB22:

- AGB23: Work in class
- AGB24:
**Assignment Presentation**

Assignment presentations: 15-20 mins presentations (all group participants should present). Presentations should include a descriptions of the problem, methodologies used, results obtained and discussion/conclusions of the work.

- AGB25, AGB26:
- AGB27, AGB28:
- AGB29, AGB30:

**Naive Bayes model to classify tissue types and brain regions using splicing patterns**Assignment description.**Markov model to predict RNA binding sites of a protein**Description.**Hidden Markov model of 5' splice-site selection**Assignment description**Hidden Markov model of the U2 branch point**Assignment description**Hidden Markov model of the U12 branch point**Assignment description

Some Perl notes.

**t.b.a.
****
**

- Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Richard Durbin, Sean R. Eddy, Anders Krogh, and Graeme Mitchison. Cambridge University Press, 1999
- Problems and Solutions in Biological Sequence Analysis.M. Borodovsky and S. Ekisheva. Cambridge University Press, 2006
- Machine Learning. Tom Mitchell. McGraw Hill 1997.
- Genomic Perl. Rex A. Dwyer. Cambridge University Press 2003.
- Combinatorial Pattern Matching Algorithms in Computational Biology using Perl and R. Gabriel Valiente. Taylor & Francis/CRC Press (2009)
- An Introduction to Bioinformatics Algorithms (Computational Molecular Biology) by Neil C. Jones and Pavel A. Pevzner. MIT press 2000.

For Markov models, Hidden Markov Models and the EM algorithm, see the excellent courses by:

- On-line books from Safari and O'Reilly. (Only accesible from the University Network).
- Learning Perl from O'Reilly.
- Programming Perl from O'Reilly.
- Beginning Perl for Bioinformatics from O'Reilly.
- Mastering Perl for Bioinformatics from O'Reilly.

- Some tutorials from www.perl.org.
- PERL programming course by Dr A D Marshall.

- GENOMES. T.A. Brown. Garland Science. 2002 (2nd Edition). (online book from NCBI).
- Human Molecular Genetics. T. Strachan and A.P. Read. Garland Science. 1999 (2nd Edition). (online book from NCBI)
- L. Hunter Molecular Biology for Computer Scientists. In Artificial Intelligence and Molecular Biology, L. Hunter editor, 1993, AAAI Press.
- L. Hunter Life and Its Molecules: A Brief Introduction. AI Magazine 25(1):9-22, 2004.
- US DOE Primer on Molecular Genetics