The course presents the fundamental principles of translational genetics and the
computational methods applied to exploit data provided by state-of-the-art
technologies (eg Whole Genome Sequencing, RNA-seq, DNA methylation assay).
The purpose is to acquaint the student with computational techniques for the
combined analysis of genetic information at the sequence level as well as the
transcriptional and translational profile and epigenetic modifications, and the
discovery of correlations with phenotypic characteristics. The course material
covers a wide range of modern computational approaches, such as translational
bioinformatics, machine learning and deep learning techniques, and computational
models for structure prediction and interaction networks prediction.

The course focuses on methodologies for the analysis of genetic diversity and
polymorphisms at the population level in terms of specific phenotypic traits through
genome-wide association studies and the calculation of the polygenic risk index, as
well as modern approaches for the computational analysis of genetic diversity in the context of genetic epidemiology. It focuses on the student's expertise in computational tools and methods for the clinical utility of electronic health records and medical data and deals with the application of artificial intelligence and machine learning algorithms to investigate genotype-phenotype associations.
Finally, it includes a more general overview of the architecture of computational
applications for the clinical diagnosis, prevention and treatment of pathologies
based on the clinical and genetic profile.

Upon successful completion of the course, the student will be able to:

• Understand the basic concepts of translational genetics.
• Be familiar with genome wide association studies, the concept of genetic linkage
  and haplotype investigation and is able to apply computational methods for
  phenotype-genotype correlation and determination of polygenic risk score.
• Have extensive knowledge of the genome organization as well as of the genetic
  interaction networks and their visualization methods.
• Understand the basic mechanisms of epigenetics and be familiar with the
  application of basic methods of searching, analyzing and predicting epigenetic
  modifications.
• Understand the basic principles of the architecture of computational genetics
  applications.
• Be familiar with the concepts of disease prevention and treatment and with the
  development, management, and utilization of electronic medical data.
• Be familiar with the use of computational tools for data retrieval and the design
  of a comprehensive pipeline for therapeutic strategies.
• Monitor the developments, understand the modern methodologies and evaluate the
  research results that arise in the field of translational genetics