457 citations · 815 across the 15 of their papers we have counts for
17 papers
Weakly Supervised Knowledge Transfer with Probabilistic Logical Reasoning for Object Detection
Martijn Oldenhof, Adam Arany, Yves Moreau +1
Training object detection models usually requires instance-level annotations, such as the positions and labels of all objects present in each image. Such supervision is unfortunate…
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…
SparseChem: Fast and accurate machine learning model for small molecules
Adam Arany, Jaak Simm, Martijn Oldenhof +1
SparseChem provides fast and accurate machine learning models for biochemical applications. Especially, the package supports very high-dimensional sparse inputs, e.g., millions of…
Self-Labeling of Fully Mediating Representations by Graph Alignment
Martijn Oldenhof, Adam Arany, Yves Moreau +1
To be able to predict a molecular graph structure () given a 2D image of a chemical compound () is a challenging problem in machine learning. We are interested to learn $f: U…
Topological Graph Neural Networks
Max Horn, Edward De Brouwer, Michael Moor +3
Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures such as cycles. We present…
Longitudinal modeling of MS patient trajectories improves predictions of disability progression
Edward De Brouwer, Thijs Becker, Yves Moreau +38
Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during…
Multilevel Gibbs Sampling for Bayesian Regression
Joris Tavernier, Jaak Simm, Adam Arany +2
Bayesian regression remains a simple but effective tool based on Bayesian inference techniques. For large-scale applications, with complicated posterior distributions, Markov Chain…
ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning
Martijn Oldenhof, Adam Arany, Yves Moreau +1
In drug discovery, knowledge of the graph structure of chemical compounds is essential. Many thousands of scientific articles in chemistry and pharmaceutical sciences have investig…
Expressive Graph Informer Networks
Jaak Simm, Adam Arany, Edward De Brouwer +1
Applying machine learning to molecules is challenging because of their natural representation as graphs rather than vectors.Several architectures have been recently proposed for de…
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
Edward De Brouwer, Jaak Simm, Adam Arany +1
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimension…
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification
Edward De Brouwer, Jaak Simm, Adam Arany +1
We present a generative approach to classify scarcely observed longitudinal patient trajectories. The available time series are represented as tensors and factorized using generati…
Two-level preconditioning for Ridge Regression
Joris Tavernier, Jaak Simm, Karl Meerbergen +1
Solving linear systems is often the computational bottleneck in real-life problems. Iterative solvers are the only option due to the complexity of direct algorithms or because the…
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…
An expanded evaluation of protein function prediction methods shows an improvement in accuracy
Yuxiang Jiang, Tal Ronnen Oron, Wyatt T Clark +144
Background: The increasing volume and variety of genotypic and phenotypic data is a major defining characteristic of modern biomedical sciences. At the same time, the limitations i…
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…
Easy Hyperparameter Search Using Optunity
Marc Claesen, Jaak Simm, Dusan Popovic +2
Optunity is a free software package dedicated to hyperparameter optimization. It contains various types of solvers, ranging from undirected methods to direct search, particle swarm…