You are all cordially invited to the AMLab colloquium today, April 12 at 16:00 in C3.163, where Philip Versteeg will give a talk titled “Causal inference and validation with micro-array data”. Afterwards there are drinks and snacks!
Abstract: In causality, one objective is to find cause and effect in a high dimensional setting. Often, identifiability of the true underlying graph is hard if not impossible when only observational data is available. A novel method, invariant causal prediction (ICP) is based on a relatively simple invariance principle. It is capable of exploiting a combination of observational and perturbed interventional experiments and provides confidence statements on the identified causal effects. In this talk, an application of ICP on high-dimensional micro-array experiments from the species Saccharomyces Cerevisiae is discussed. An attempt is made to validate the findings both internally and with external results.
Tom Claassen joined AMLab as a parttime postdoc (50%). Tom studied physics in Twente and worked for several years as a Systems Architect before doing his PhD on causal discovery and logic at the Radboud University Nijmegen. Tom will work on causality as a team member of the VIDI project of Joris Mooij.
You are all cordially invited to a presentation on Friday, April 8th, from 16:00-17:00 in C1.112 by Martin Gullaksen on his master’s thesis entitled “Probabilistic Spatio-Temporal Inference in Early Embryonic Development. The case of Drosophila Melanogaster“.
Abstract: Being able to infer gene regulatory networks from spatio-temporal expression
data is a major problem in biology. This thesis proposes a new dynamic Bayes
networks approach, which we benchmark by using the well researched gap gene
problem of the Drosophila melanogaster, with the capability of realistically
inferring gene regulatory networks and producing high quality simulations. The
thesis solves practical issues, currently associated with spatio-temporal gene
inference, such as computational time and parameter fragility, while obtaining
a similar gene regulatory network and matrix as our ground truth network. The
proposed modelling framework computes the gene regulatory network in 10-15
second on a modern laptop. Effectively removing the computational barrier of
the problem and allowing for future gene regulatory networks of greater gene
count to be processed. Besides producing a gene regulatory matrix our method
also produces high quality simulations of the gene activation levels of the gap
gene problem. In addition, unlike many competing problem formulations, the
proposed model is probabilistic in nature, hence allowing statistical inference
to be made. Finally, using Bayesian statistics, we perform robustness tests on
the topology of our proposed gene regulatory network and our regulatory