The PhD course in Innovative Biomedical Technologies in Clinical Medicine is designed to carry out a training program in translational medicine. To this end, particular attention is paid not only to advanced biotechnologies (like NGS), but also to the ability to manage and analyze the large amount of data derived from them (Big Data Analysis) through innovative bioinformatics tools taking into account the overall complexity of a disease (Network Medicine). This is crucial for complex diseases which are determined not only by specific molecular alterations but also by perturbations extended to whole biological systems, both at the individual and the population level. On the other hand, an effective use of these technologies requires adequate knowledge and familiarity with clinical models, which can allow foreseeing the application of scientific observations to the clinical setting. This requires the creation of a new profile of researcher with specific skills not only for using innovative technologies in the investigation of molecular mechanisms of diseases but also for applying these knowledge to the diagnosis, prognosis and therapy of specific clinical conditions. Therefore, the main purposes of the doctorate are: a) to respond to the increased demand for new models of teaching preparing to the so-called translational medicine; b) to achieve a better integration between basic and clinical knowledge as well as bioinformatics and computational biology; c) to create a new professional figure that is expert in translational medicine who is able to combine the expertise in the use of advanced technologies with the clinical skills necessary to identify both the most relevant scientific issues and the potential applications of new knowledge; The specific educational objectives of the PhD program are the following: a) learning epidemiological and statistical elements of genetic susceptibility in multifactorial diseases (complex traits); b) learning the genomic study methodologies and its variability (genomic analysis of SNPs and CNV; techniques for DNA methylation analysis; microbial study; next-generation sequencing (NGS) techniques for genome and/or exome] and bioinformatics methodologies underlying the evaluation of genetic research results [design and analysis of genome-wide association studies (GWAS); design and analysis of NGS studies: panel design for molecular characterization of specific diseases, experimental design for whole genome/exome sequencing, sequence alignment, variant calling, variant prioritization, in silico prediction of the effects of mutations]; c) learning of the methods of studying the epigenetic modifications of the chromatin and techniques of DNA methylation analysis; d) learning the gene expression study methods (mRNA, miRNA and lncRNA) [arrays; digital-PCR; RNA-seq; miRNA-seq]; e) learning of cell culture techniques, including the isolation, characterization and culturing of progenitor cells, stem cells and iPS; organoid cultures, innovative culture system which recapitulate the in vivo architecture, functionality, and genetic signature of original tissues; f) learning techniques for in vitro and ex vivo cells of innate and adaptive immunity; g) learning of the main genetic modification genetic and conditioning genetic methods [transgenic; knock-out; Knock-in]; h) learning of proteomic and metabolomic techniques for identifying disease predictors; i) learning the principles and applications of network medicine [integration of different datasets; identification of modules, hubs and nodes; generation of interactome models to infer and assess function, to understand mechanisms, and to prioritize candidates for further investigation; relationship between molecular and phenotypic networks to understand the determinants of disease expression]; j) learning and applying innovative imaging techniques; k) learning the principles of pharmacology with particular reference to pharmacogenetic and drug-genomic knowledge, drug-prevention; l) apprehending the principles and applications of BigData Science [generation and manipulation of massive datasets from clinical and/or genomic data, design of big data algorithms]; m) learning the basics of clinical trials (experimental design, statistical estimation, statistical tests and decision rules, controlled trials, randomization methods, double blind experiments, etc.), experimental data management as well as publication and interpretation of results; n) learning the basics of in silico clinical trials (development of computer simulation models in experimental physiology, pathophysiology, pharmacokinetics and pharmacodynamics; development of computer simulation in the evaluation of a medicinal product, medical device, or a personalized medical treatment); o) learning the principles of experimentation on animals included; elements of biomedical ethics; p) assessment of the business and industrial aspects of advanced biotechnology.