Introduction to Machine learning for Bioinformatics and Life Sciences

Period of duration of course
Course info
Number of course hours
Number of hours of lecturers of reference
Number of hours of supplementary teaching

Type of exam

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


Module 1 (20 hours) Foundation of Data Mining and machine Learning (Prof. Giannotti) 

1) Introduction: the Knowledge Discovery process. All steps in a nutshell

2) Data understanding and Data exploration

3) Unsupervised learning: Clustering: intro clustering: K-Means clustering, hints on DBSCAN e Hierarchical clustering

4)  Unsupervised learning: Pattern mining: a-priori pattern mining: case study 

5)  Practicals: case study  on simple data sets iris e titanic (python or Knime)

6) Supervised Learning: Classification introduction, performance evaluation, a first simple classifier: Decision tree  

7) practical: case study on simple data sets iris e titanic (python or Knime)

9) Supervised Learning: Classification overview on advanced methods: Random Forst, Support Vector Machine, Neural Networks

10)  Supervised Learning: Deep Learning with Recurrent Neural Networks: architecture and exemplar usecase

11)  Supervised Learning: Deep Learning with Generative adversarial network: architecture and exemplar usecase

12) Supervised Learning:  Deep Learning with Trasformers: architecture and exemplar usage

Module 2 (20hrs) 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 protein 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

Berthold et. al. Guide to Intelligent Data Analysis : How to Intelligently make sense of real data

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

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

Ad hoc selected scientific papers