18 citations · 23 across the 6 of their papers we have counts for
7 papers
BITES: Balanced Individual Treatment Effect for Survival data
Stefan Schrod, Andreas Schäfer, Stefan Solbrig +7
Estimating the effects of interventions on patient outcome is one of the key aspects of personalized medicine. Their inference is often challenged by the fact that the training dat…
Differentiated uniformization: A new method for inferring Markov chains on combinatorial state spaces including stochastic epidemic models
Kevin Rupp, Rudolf Schill, Jonas Süskind +6
Motivation: We consider continuous-time Markov chains that describe the stochastic evolution of a dynamical system by a transition-rate matrix which depends on a parameter .…
Low-rank tensor methods for Markov chains with applications to tumor progression models
Peter Georg, Lars Grasedyck, Maren Klever +3
Continuous-time Markov chains describing interacting processes exhibit a state space that grows exponentially in the number of processes. This state-space explosion renders the com…
Lyra -- containerized microservices for browsing shared biomedical data
Michael Huttner, Claudio Lottaz, Christian Kohler +1
Research papers in the biomedical field come with large and complex data sets that are shared with the scientific community as unstructured data files via public data repositories.…
Fully integrative data analysis of NMR metabolic fingerprints with comprehensive patient data: a case report based on the German Chronic Kidney Disease (GCKD) study
Helena U. Zacharias, Michael Altenbuchinger, Stefan Solbrig +11
Omics data facilitate the gain of novel insights into the pathophysiology of diseases and, consequently, their diagnosis, treatment, and prevention. To that end, it is necessary to…
Loss-function learning for digital tissue deconvolution
Franziska Görtler, Stefan Solbrig, Tilo Wettig +3
The gene expression profile of a tissue averages the expression profiles of all cells in this tissue. Digital tissue deconvolution (DTD) addresses the following inverse problem: Gi…