23 citations · 49 across the 4 of their papers we have counts for
4 papers
Industry-Scale Orchestrated Federated Learning for Drug Discovery
Martijn Oldenhof, Gergely Ács, Balázs Pejó +44
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), whic…
Fast semi-supervised discriminant analysis for binary classification of large data-sets
Joris Tavernier, Jaak Simm, Karl Meerbergen +3
High-dimensional data requires scalable algorithms. We propose and analyze three scalable and related algorithms for semi-supervised discriminant analysis (SDA). These methods are…
Highly Scalable Tensor Factorization for Prediction of Drug-Protein Interaction Type
Adam Arany, Jaak Simm, Pooya Zakeri +5
The understanding of the type of inhibitory interaction plays an important role in drug design. Therefore, researchers are interested to know whether a drug has competitive or non-…
Macau: Scalable Bayesian Multi-relational Factorization with Side Information using MCMC
Jaak Simm, Adam Arany, Pooya Zakeri +5
We propose Macau, a powerful and flexible Bayesian factorization method for heterogeneous data. Our model can factorize any set of entities and relations that can be represented by…