Machine Learning for the Life Sciences

Oral exam

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)

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