Introduction to Machine learning for Bioinformatics and Life Sciences

Period of duration of course
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Course info
Number of course hours
46
Number of hours of lecturers of reference
40
Number of hours of supplementary teaching
0
CFU 6
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Type of exam

Oral exam

Lecturer

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Lecturer

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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

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. 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/

Ad hoc selected scientific papers