Studying at the University of Verona
Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.
Study Plan
This information is intended exclusively for students already enrolled in this course.If you are a new student interested in enrolling, you can find information about the course of study on the course page:
Laurea in Bioinformatica - Enrollment from 2025/2026The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.
1° Year
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2° Year activated in the A.Y. 2019/2020
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1 module to be chosen among the following3° Year activated in the A.Y. 2020/2021
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1 module to be chosen among the following| Modules | Credits | TAF | SSD |
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1 module to be chosen among the following| Modules | Credits | TAF | SSD |
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1 module to be chosen among the followingLegend | Type of training activity (TTA)
TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.
Discrete Biological Models (2020/2021)
Teaching code
4S01908
Credits
6
Language
Italian
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
The teaching is organized as follows:
Teoria
Laboratorio
Learning outcomes
The aim of the course is to present methods from discrete mathematics which are employed in the analysis of biological phenomena, with a major emphasis on the computational analysis of genomes. At the end of the course the students will be able to apply discrete probability and information theoretic tools for analysing genomic data.
Program
Basic notation on sequences and strings. The problems of substring and superstring. Lexicographic ordering and suffix arrays. Advanced combinatorial schemata and discrete probability. Random sequences and fundamental probability laws on them (Bernoulli, Poisson, Exponential, Gauss). Information sources and entropy. Conditional entropy, entropic divergences and mutual information. Genomes, get genomic indexes, genomic dictionaries, genomic distributions and entropies. Representations and visualizations of genomes. Introduction to python3. Development of algorithms and data structure for genomics in python3.
Bibliography
| Activity | Author | Title | Publishing house | Year | ISBN | Notes |
|---|---|---|---|---|---|---|
| Teoria | T. M. Cover, J. A. Thomas | Elements of Information Theory (Edizione 1) | John Wiley & Sons, Inc. | 1991 | 0471062596 | |
| Teoria | Vincenzo Manca | Infobiotics | Springer | 2013 | ||
| Laboratorio | Sebastian Bassi | Python for Bioinformatics (Edizione 2) | Routledge | 2017 | 1138035262 |
Examination Methods
The exam consists of an oral examination plus the development of a project.
The oral exam covers the entire course program and the assessment is expressed with a vote from 0 to 30.
The project is agreed upon with the student starting from a list of projects proposed by the teacher. The evaluation of the project is expressed by a vote from 0 to 30.
The final grade is the average of the two assessments, oral exam and project.
