Van Tien Nguyen

Martijn Oldenhof, Gergely Ács, Balázs Pejó et al.

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper.

Van Tien Nguyen, Christian Sallaberry, Mauro Gaio

We propose in this paper a method for measuring the similarity between ontological concepts and terms. Our metric can take into account not only the common words of two strings to compare but also other features such as the position of the words in these strings, or the number of deletion, insertion or replacement of words required for the construction of one of the two strings from each other. The proposed method was then used to determine the ontological concepts which are equivalent to the terms that qualify toponymes. It aims to find the topographical type of the toponyme.