1 code implementation • 18 Dec 2020 • Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
In this paper, we propose a Bayesian approach called BayesMR that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation.
no code implementations • 18 Dec 2020 • Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Unfortunately, searching for proper instruments in a many-dimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid, so most existing search methods either rely on overly stringent modeling assumptions or fail to capture the inherent model uncertainty in the selection process.
1 code implementation • NeurIPS 2020 • Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence.
1 code implementation • 3 Sep 2019 • Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Many of these computational methods are designed to infer individual regulatory relationships among genes from data on gene expression.
1 code implementation • 18 Sep 2018 • Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data.
2 code implementations • 12 Jun 2018 • Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes
We consider the problem of learning parameters of latent variable models from mixed (continuous and ordinal) data with missing values.
1 code implementation • 6 Apr 2017 • Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Causal effect estimation from observational data is an important and much studied research topic.