Machine Learning for the Life Sciences

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Modalità esame

Oral exam


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Students with no background in python are recommended to attend the course "Scientific Programming I: Data Processing and Software Prototyping" by Prof. Bloino


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 structure prediction using AI

4)      Protein language models: protein function prediction 

5)      Graph neural network to model context-dependent emerging properties 

6)      Deep Learning for genomics and multi-omics integration

7)      practicals: a) functional predictions with protein language models;b) deep learning for genomics (Transcription factor binding prediction); c) multiomics data integration (python)

Obiettivi formativi

Aim of the course is to provide students with basic knowledge of both theoretical foundations and practical aspects of machine learning, with a particular focus on applications to bioinformatics and biology.

Riferimenti bibliografici

Introduction to Machine Learning, Lecture notes. MIT, 2019.

Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning. MIT Press, 2016.

Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Deep Learning for the Life Sciences, 2019,

Ad hoc selected scientific papers