Engineering in Computer Science and Artificial Intelligence

General outcomes: The course will focus on statistical and unsupervised data mining methods for medicine. Students will acquire basic biological knowledge, knowledge of major biological databases and data analysis tools, bioinformatics skills and familiarity with omics data analysis. Specific outcomes: Knowledge and understanding: Students become familiar with basic biological concepts, R programming applied to bioinformatics, the analysis of gene expression data using statistical and unsupervised methods for the investigation of complex diseases. Applying knowledge and understanding: Students will be able to perform a standard bioinformatic analysis by applying the statistical techniques acquired during the course to identify modulated molecules potentially characterizing a disease phenotype. Making judgements: Students will be able to evaluate the quality of the performed data analysis, characterizing the results through the investigation tools presented during the course and seeking for literature-based evidence of the obtained results. Communication skills: The course includes practical sessions and a final project activity that will allow the student to be able to understand, present and adequately discuss the results obtained from a basic bioinformatics data analysis carried out on real case studies, as well as communicate and justify the methodological and parameter choices used to accomplish this analysis. Learning skills The course includes theoretical lessons that will allow the student to develop the usual learning skills from the theoretical study of the teaching material, and practical sessions, in particular project activities on real case studies of molecular data analysis relating to various pathologies, thus stimulating the student both to independently study some of the topics presented in the course and to concretely apply the notions and techniques learned during the course.