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:
Master's degree in Medical Bioinformatics - 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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3 courses among the following2° Year activated in the A.Y. 2025/2026
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3 courses among the following| Modules | Credits | TAF | SSD |
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3 courses among the following| Modules | Credits | TAF | SSD |
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3 courses among the following| Modules | Credits | TAF | SSD |
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Legend | 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.
Analisi di dati Multi-omics da single-cell (2025/2026)
Teaching code
4S009832
Teacher
Coordinator
Credits
6
Language
English
Scientific Disciplinary Sector (SSD)
INF/01 - INFORMATICS
Period
1st semester dal Oct 1, 2025 al Jan 30, 2026.
Courses Single
Authorized
Learning objectives
Knowledge and understanding The course aims to provide students with the knowledge and understanding of the algorithms and advanced programming tools for the management of single cell and spatial transcriptomic data. Applying knowledge and understanding The student will therefore be able to a) apply the algorithms and advanced programming tools for the analysis of single cell and spatial transcriptomic data; b) apply the code performance analysis and identify critical issues and their optimization. Making judgements Ability to independently propose effective and efficient solutions for the biomedical and bioinformatics application domain; ability to identify critical issues for the treatment of complex bioinformatics problems related to the analysis of single cell and spatial transcriptomic data. Communication The student will also be able to interact with various interlocutors in a multidisciplinary biomedical and bioinformatics context, to interact with colleagues in the performance of group work, and to interact with the interlocutors in the working or research environment. Lifelong learning skills Ability to understand scientific literature in the process of interpreting the results or proposed solution, and to carry out individual and group in-depth studies aimed at tackling problems from the research and business world.
Prerequisites and basic notions
Conceptual and practical notions of programming languages
Program
The course covers advanced data structures and algorithms for the analysis of:
single-cell data,
spatial transcriptomics data,
multi-omics data.
Complex deep learning models will be introduced and implemented, with a balance between theory and practical application. The methodologies will be applied to real case studies presented by experts in the biomedical field.
Programming languages: R and Python.
The course will be enriched by seminars from international experts, focusing on the computational aspects of analysis and the integration of multi-omics data.
Didactic methods
Students will follow theoretical lessons and exercises whose content will be provided via notebooks. Students will install and use the software related to the chosen topics and will analyze real cases.
Learning assessment procedures
Development of a project in the classroom (or remotely to be evaluated with the teacher).
Evaluation criteria
Explanation, through presentation and demo, critical and detailed, of the methodologies used for the development of the project and of the biomedical results obtained. The presentation can take place in front of experts of the applications dealt with.
Criteria for the composition of the final grade
The grade is composed of the evaluation of three factors: autonomy, competence, critical sense that the student has shown during the development of the project and during the presentation.
Exam language
English
