Machine Learning for the Life Sciences
Prerequisites
Students with no background in python are recommended to attend the course "Scientific Programming I: Data Processing and Software Prototyping" by Prof. Bloino
Programme
Application of Machine Learning algorithms in Bioinformatics and Life Sciences (Dr. Raimondi)
1) protein structure/function prediction using machine learning
2) protein structure prediction using deep learning and co-evolution
3) protein language models
4) application of graph neural network for the prediction of biomolecular properties and interaction networks
5) deep learning applications to genomics :DNA motif discovery
6) deep learning applications to genomics: variant intepretation
7) deep learning applications to genomics: single cell RNAseq analysis and interpretation
8) practicals: a) functional predictions with protein language models;b) deep learning for genomics (Transcription factor binding prediction); c) multiomics data integration (python)
Educational aims
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.
Bibliographical references
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