Machine learning in Bioinformatics: Novel approaches to Precision Medicine, Life Sciences and Healthcare

In recent years, biological research revolves around huge amounts of data which are extrapolated due to high-throughput techniques. Thanks to the emergence of omics information and big data, the use of computational tools has become crucial to evaluate the efficacy of medical treatments or deeply investigate the correlation between patients and diseases according to their own molecular characteristics. The Precision Medicine approach is widely applied to the healthcare area, in particular to rare diseases with the creation of patient registries leveraging large amounts of data to discover potential links. Harmonizing databases and including disease registries are the major facilitators to understand the complexity of diseases, to conduct clinical trials, to improve the drug development process and to assign the right treatment to the right individual after a reliable patient stratification. Moreover, the application of data mining in healthcare and public health, which has been growing over the last years, allows to systematically identify inefficiencies and best practices that improve care and reduce costs with remarkable economic benefits. In this thesis we focus on the development of new Artificial Intelligence algorithms for a number of important problems in the field of Precision Medicine, Life Sciences and Healthcare. The project demonstrates the power of computational modelling for clinical research, opening up possibilities that would be unimaginable without knowledge of the data. The application of Bioinformatics and Computational biology algorithms together with the creation of digital databases will offer an opportunity to translate new data into actionable information.