OVERVIEW

The Bioinformatics & Computational Biology course aims at teaching the statistical tools for tackling biomedical data analysis tasks in very high dimension. 

The course will consist in classes and guided labs.

The course has a strong applicative connotation. In addition to the labs, the student will work on a project that will require a great deal of autonomy to solve complex problems.

AIMS AND CONTENT

LEARNING OUTCOMES

Students will learn basic elements in pipeline of high-throughput data analysis: crash course on molecular biology; overview on sequencing technologies; alignment and normalization algorithms; QC criteria unsupervised and supervised learning methods for subtyping and data exploration as well as variable selection and functional characterisation; network reconstruction algorithms. Students will be involved in project activities.

TEACHING METHODS

Class (44 hours), lab (12 hours), project (50 hrs) and outside preparation
Students are required to attend class and lab for a total of 6 hrs/week

SYLLABUS/CONTENT

In the Bioinformatics and Computational Biology course, students will learn about:

• biomedical data science and molecular data
• normalization and preprocessing of high-throughput molecular data
• selection bias
• dimensionality reduction techniques
• variable selection methods: filter, wrapper and embedded
• enrichment analysis 
• molecular network inference
• dictionary learning

TEACHERS AND EXAM BOARD

Exam Board

ANNALISA BARLA (President)

SAMUELE FIORINI

FRANCESCO MASULLI

STEFANO ROVETTA

FEDERICO TOMASI

VERONICA TOZZO

ALESSANDRO VERRI

LESSONS

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The final evaluation will take into account: (1) class attendance, (2) planned homework, (3) project discussion (4) oral dissertation

ASSESSMENT METHODS

The project should be written clearly, complemented with working code and it should show that the student has fully understood the topic. Examples on different real scenarios are encouraged.

The oral examination consists in a discussion of the project and of the topics taught in class. 

Exam schedule