BIOINFORMATICS AND NETWORK MEDICINE

Obiettivi formativi

General objectives. The general objectives of the course are: i) to provide students with a hands-on experience with basic biological concepts and common bioinformatics tools and databases; ii) to introduce students to the on-the-field application of networks in biology and medicine. Specific objectives. Students are expected to acquire basic biology knowledge and skills, to understand the role of networks in the study of physiological mechanisms and diseases; to understand how to use network medicine algorithms and procedures. Knowledge and understanding. The course will include theory and hands-on projects. Students will be trained in the basic theory and application of programs used for database searching, biological network inference and analysis. Apply knowledge and understanding. At the end of the course students will have become familiar with basic biological concepts and bioinformatics databases and tools. Furthermore, on successful completion of this course, students will understand the use of networks as a paradigm for disease expression and course. Critical and judgment skills. At the end of the course, students will be able to critically analyse the results of their analysis. Communication skills. The students will be required to produce reports describing the hands-on projects with specific sections for the description of the obtained results and their discussion. Learning ability. The projects will be developed in small groups encouraging team building. All the acquired abilities will be checked in a final oral exam during which a good division of teamwork will be rewarded.

Canale 1
MANUELA PETTI Scheda docente

Programmi - Frequenza - Esami

Programma
- Course intro: what Network Medicine is and why you should follow this course - Crash course in Molecular Biology: what you absolutely need to know about the basics of life and diseases in these pandemic times (and to truly understand what you are doing with all these genes and proteins) - Where do we collect our data, and how are they made? Key biological databases and datasets - Network science and molecular data: a powerful partnership in a heuristic key - Disease gene prediction: how we can leverage existing data via networks to generate new associations and new knowledge - Drug repurposing: using (almost) everything we know to reuse existing drugs for new diseases - Hands-on lectures: python NetworkX, human interactome analysis, specially designed standalone apps, application of ML and other algorithms for disease gene prediction and drug repurposing - Seminar: The Scientific Method, Technical & Scientific writing, Psychometric Network Analysis
Prerequisiti
Prerequisiti non richiesti
Testi di riferimento
Il materiale didattico in formato elettronico (dispense, codice) sarà distribuito dai docenti
Frequenza
Frequenza consigliata, ma non obbligatoria
Modalità di esame
La valutazione della preparazione avverrà in due prove: - prova progettuale per valutare la capacità di applicare i concetti teorici acquisiti durante il corso - prova orale per valutare il grado di approfondimento dello studio della materia
Modalità di erogazione
The course includes theoretical and hand-on lectures and seminars
  • Codice insegnamento10593052
  • Anno accademico2025/2026
  • CorsoData Science
  • CurriculumCurriculum unico
  • Anno2º anno
  • Semestre1º semestre
  • SSDING-INF/06
  • CFU6