- Docente: Pier Luigi Martelli
- Credits: 3
- SSD: BIO/10
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LS) in Bioinformatics (cod. 0443)
Learning outcomes
Theory of machine learnig systems and application to the prediction
of structural and functional features of protein and nucleotidic
sequences.
Course contents
Basics on Probability Theory
Bayes Theory
Neural networks
Application of neural networks to the problem of protein structure
prediction
Markov Models
Hidden Markov Models
Applications of HMMs to the sequence alignment problem (with
practicals)
Building of HMMs applied to the alignment problems: HMMer, PFAM
(with practicals)
Applications of HMMs to the prediction problem (topology of
membrane proteins, gene structure) (with practicals)
Support Vector Machines
Applications of SVMs to the prediction problem (with
practicals)
Readings/Bibliography
Durbin R, Eddy S, Krogh A, Mitchison G (1998) Biological Sequence
Analysis:
Probabilistic Models of Proteins and Nucleic Acids. Cambridge
University Press [ISBN 0-521-62971-3]
Teaching methods
Lectures
Assessment methods
Oral examination
Teaching tools
Use of free tools (HMMER)
Use of web-based tools
Office hours
See the website of Pier Luigi Martelli