Modalità d'esame
Development of a project and presentation
Prerequisiti
Students are required to attend the course "Introduzione al Machine Learning" by prof. GiannottiStudents with no background in python are recommended to attend the course "Scientific Programming I: Data Processing and Software Prototyping" by Prof. Bloino
Programma insegnamento
Application of Machine Learning algorithms in Bioinformatics and Life Sciences (Prof. Raimondi)
1) Introduction:Key aspects of ML/AI for the life sciences
2) Protein language models: protein function prediction
3) Protein structure prediction using AI
4) Deep Learning for genomics and multi-omics integration
5) practicals: a) functional predictions with protein language models;b) deep learning for genomics (Transcription factor binding prediction); c) multiomics data integration (python)
Riferimenti bibliografici
Introduction to Machine Learning, Lecture notes. MIT, 2019. https://phillipi.github.io/6.882/2020/notes/6.036_notes.pdf Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning. MIT Press, 2016.
https://www.deeplearningbook.org/ Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Deep Learning for the Life Sciences, 2019, https://www.oreilly.com/library/view/deep-learning-for/9781492039822/
Ad hoc selected scientific papers