Statistical Sciences

LORENZO FARINA

Learning goals
The course aims at providing students with a basic training in practical and theoretical bioinformatics using the most common models and tools for analyzing "omics" data in biology and molecular medicine. After completing the course, the student is expected to be able to analyze and interpret large-scale data such as, for example, a patient's transcriptomic data using appropriate methodologies implemented with matlab or other high-level programming languages . Furthermore, the student will be able to link the biological theory and the analysis techniques critically analyzing the results.
Knowledge and understanding
Students will acquire knowledge and understanding in the field of bioinformatics and computational medicine at a level including some innovative topics.
Applying knowledge and understanding
Students will be able to apply their knowledge and show familiarity with methods in bioinformatics and computational medicine; they will also acquire adequate skills both to support arguments, and to solve problems typical to the subject.
Making judgements
Students will be able to collect, analyse and interpret data in the field of bioinformatics and computational medicine, to produce autonomous judgments and critical reflections of the corresponding research themes.
Communication skills
Students will be able to communicate information, ideas, problems and solutions even to non-specialist research staff (MDs/biologists).
Learning skills
Students will develop those learning skills necessary to move towards continuous in-depth studies that characterize the rapid evolution of the discipline with a high degree of autonomy.

Learning goals
The course aims at providing students with a basic training in practical and theoretical bioinformatics using the most common models and tools for analyzing "omics" data in biology and molecular medicine. After completing the course, the student is expected to be able to analyze and interpret large-scale data such as, for example, a patient's transcriptomic data using appropriate methodologies implemented with matlab or other high-level programming languages . Furthermore, the student will be able to link the biological theory and the analysis techniques critically analyzing the results.
Knowledge and understanding
Students will acquire knowledge and understanding in the field of bioinformatics and computational medicine at a level including some innovative topics.
Applying knowledge and understanding
Students will be able to apply their knowledge and show familiarity with methods in bioinformatics and computational medicine; they will also acquire adequate skills both to support arguments, and to solve problems typical to the subject.
Making judgements
Students will be able to collect, analyse and interpret data in the field of bioinformatics and computational medicine, to produce autonomous judgments and critical reflections of the corresponding research themes.
Communication skills
Students will be able to communicate information, ideas, problems and solutions even to non-specialist research staff (MDs/biologists).
Learning skills
Students will develop those learning skills necessary to move towards continuous in-depth studies that characterize the rapid evolution of the discipline with a high degree of autonomy.

Basic statistics

Bioinformatics: molecular data types and databases. Gene expression analysis. Introduction to complex networks. Interaction networks, association networks. Network medicine.

Readouts provided by the teacher

https://pubmed.ncbi.nlm.nih.gov/21164525/

The teaching of the Bioinformatics course is designed to integrate theory and practice, promoting active and progressive learning. Lectures form the core of the transmission of fundamental concepts: during these sessions, the instructor introduces theoretical principles, presents bioinformatic methodologies and tools, and guides students through concrete examples drawn from biological research.

To support the lectures, practical exercises allow students to apply what they have learned, using specialized software for the analysis of genomic, proteomic, or biological interaction network data. During these activities, students are encouraged to solve problems, interpret real datasets, and engage with complex scenarios, thereby developing operational autonomy and critical reasoning skills.

Additionally, the course includes moments of discussion and interaction in the classroom, where students can ask questions, discuss case studies, and explore topics of personal interest or current scientific relevance. Teaching materials, handouts, and online resources complement the educational offer, enabling autonomous and integrative study.

In summary, the teaching combines theoretical lectures, practical activities, guided discussions, and individual study, creating a coherent and multidimensional learning path aimed at developing knowledge-based, analytical, and applied skills in line with the course objectives.

Attending the class is suggested

During the oral exam in Bioinformatics, the assessment of the student’s knowledge is structured in several phases designed to evaluate both theoretical mastery and the ability to apply concepts to concrete situations. The interview begins with open-ended questions on the main topics of the course, such as the fundamentals of bioinformatics, the modeling of biological networks, and the analysis of genomic or proteomic data, with the aim of verifying the understanding of core concepts.

Subsequently, the instructor poses in-depth questions that require connecting different ideas, analyzing complex scenarios, or explaining the practical use of bioinformatic methods and tools. The student may also be asked to discuss practical cases or interpret real datasets, thereby demonstrating the ability to apply theory to real problems and draw evidence-based conclusions from data. Finally, the oral exam assesses synthesis and communication skills: the student must be able to present a complex topic clearly, coherently, and scientifically accurately, using the appropriate technical language.

The evaluation of knowledge is based on criteria of completeness, accuracy, depth of analysis, logical coherence, and contextualization. In this way, the oral exam measures not only what the student knows but also how effectively they can use, argue, and communicate that knowledge.

- Discuss the role of mathematics and statistics in biology and medicine
- List the main techniques for evaluating the modularity of a biological network
- Discuss the reasons for considering network medicine as a language
- Discuss the usefulness of the community concept and the corresponding algortims
- Discuss the usefulness of neural networks in precision medicine

• Lectures (72 hrs)
  • Books: Conceptual framework of bioinformatics and computational medicine