6 citations · 6 across the 3 of their papers we have counts for
7 papers
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…
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…