- Docente: Pier Luigi Martelli
- Crediti formativi: 6
- SSD: BIO/10
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
- Corso: Laurea Magistrale in Bioinformatics (cod. 8020)
-
dal 09/09/2024 al 30/10/2024
Conoscenze e abilità da conseguire
At the end of the course, the student acquires advanced machine learning based approaches (Support Vector Machine, Conditional Random Fields, Hybrid methods) to complement previous expertise. Problems of Systems Biology will be introduced with focusing on network theory and dynamic modeling to approach complexity at the cell level. In particular, the student will be able to: - understand and modeling biological complexity; - modeling time evolution of a biological system; - predicting protein-protein interaction and DNA/RNA protein interaction.
Contenuti
MACHINE LEARNING TOOLS FOR BIOINFORMATICS
Neural Networks
Support Vector Machines
Kernel methods
Deep learning methods
Decision trees and Random Forests
Applications to protein structure and function prediction
INTRODUCTION TO SYSTEMS BIOLOGY
Biological Systems
Experimental Techniques
Genomics, Proteomics, Interactomics, Transcriptomics, Metabolomics, Metagenomics, Epigenomics
Basics on Model
Mathematical Methods: Networks
Mathematical Methods: Differential equations
Kinetics of biochemical reactions and simple metabolic pathways
Transcription networks in Prokariotes.
Analysis of simple motifs (self-regulation, Feed-forward loops)
Testi/Bibliografia
Slides of the lecture and papers cited therein
Suggested books for a deeper study:
Bishop C (2006) Pattern recognition and Machine Learning. Springer
[ISBN 0-38-731073-8]
Goodfellow I, Bengio Y, Courville A. Deep Learning (2016) MIT Press [ISBN: 9780262035613]
Ingalls BP (2013) Mathematical Modeling in Systems Biology.
MIT press [ISBN: 9780262018883]
Aron U. (2006) An Introduction to Systems Biology: Design Principles of Biological Circuits. Chapman & Hall/CRC Mathematical and Computational Biology (Vol. 10) [ISBN-13: 9781584886426]
Metodi didattici
Lezioni frontali
In considerazione della tipologia di attività e dei metodi didattici adottati, la frequenza di questa attività formativa richiede la preventiva partecipazione di tutti gli studenti ai Moduli 1 e 2 di formazione sulla sicurezza nei luoghi di studio, [https://elearning-sicurezza.unibo.it/] in modalità e-learning.
Modalità di verifica e valutazione dell'apprendimento
The final exam consists of a written test followed, if necessary, by an oral discussion.
It aims at assessing the achievement of the learning goals of the course:
- the knowledge of the theory and applications of machine learning tools for Bioinformatics
- the knowledge of the theory of complex networks and their application to the description of biological systems;
- the analysis and integration of omics data
- the knowledge of the basic theory of ordinary differential equations and their application to the description of biological systems.
Strumenti a supporto della didattica
Orario di ricevimento
Consulta il sito web di Pier Luigi Martelli
SDGs


L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.