MOTIVATION: Rare diseases collectively affect 5% of the population. However, fewer than 50% of rare disease patients receive a molecular diagnosis after whole genome sequencing. Supervised machine learning is a valuable approach for the pathogenicity scoring of human genetic variants. However, existing methods are often trained on curated but limited central repositories, resulting in poor accuracy when tested on external cohorts. Yet, large collections of variants generated at hospitals and research institutions remain inaccessible to machine-learning purposes because of privacy and legal constraints. Federated learning (FL) algorithms have been recently developed enabling institutions to collaboratively train models without sharing their local datasets. RESULTS: Here, we present a proof-of-concept study evaluating the effectiveness of FL for the clinical classification of genetic variants. A comprehensive array of diverse FL strategies was assessed for coding and non-coding Single Nucleotide Variants as well as Copy Number Variants. Our results showed that federated models generally achieved comparable or superior performance to traditional centralized learning. In addition, federated models reached a robust generalization to independent sets with smaller data fractions as compared to their centralized model counterparts. Our findings support the adoption of FL to establish secure multi-institutional collaborations in human variant interpretation. AVAILABILITY AND IMPLEMENTATION: All source code required to reproduce the results presented in this article, implemented in Python, is available under the GNU General Public License v3 at https://github.com/RausellLab/FedLearnVar.

This paper describes the experience of the Cartagena node in the 2015 edition of the EU-xCEL project, which was funded by the European Union's Horizon 2020 Research and Innovation Programme. EU-xCEL aims at fostering entrepreneurship awareness and training among young graduates, in Information and Communication Technologies as well as to strengthen the European entrepreneurship ecosystem. This paper outlines the development of the training week and the posterior virtual incubation phase, as well as the experience of running the program in Cartagena. Finally, we outline some conclusions drawn from the experience.

This paper presents the evolution of a startup ecosystem at a technological university both for teaching engineering students about entrepreneurship-related issues and for offering them another professional alternative. After three years of operation at a regional level, the ecosystem is connected with other European ecosystems by means of the Startup Europe initiative, thereby gaining better results with regard to the number of accelerated startups, the scope of the experience and future possibilities for enterprising students.