1 code implementation • 28 Nov 2023 • Stefan Schrod, Fabian Sinz, Michael Altenbuchinger
The development of causal prediction models is challenged by the fact that the outcome is only observable for the applied (factual) intervention and not for its alternatives (the so-called counterfactuals); in medicine we only know patients' survival for the administered drug and not for other therapeutic options.
1 code implementation • 1 Jun 2023 • Stefan Schrod, Jonas Lippl, Andreas Schäfer, Michael Altenbuchinger
We propose Federated Adversarial Cross Training (FACT), which uses the implicit domain differences between source clients to identify domain shifts in the target domain.
Federated Learning Multi-Source Unsupervised Domain Adaptation +2
1 code implementation • 5 Jan 2022 • Stefan Schrod, Andreas Schäfer, Stefan Solbrig, Robert Lohmayer, Wolfram Gronwald, Peter J. Oefner, Tim Beißbarth, Rainer Spang, Helena U. Zacharias, Michael Altenbuchinger
Their inference is often challenged by the fact that the training data comprises only the outcome for the administered treatment, and not for alternative treatments (the so-called counterfactual outcomes).
1 code implementation • 4 Apr 2021 • Katherine H. Shutta, Deborah Weighill, Rebekka Burkholz, Marouen Ben Guebila, Dawn L. DeMeo, Helena U. Zacharias, John Quackenbush, Michael Altenbuchinger
The increasing quantity of multi-omics data, such as methylomic and transcriptomic profiles, collected on the same specimen, or even on the same cell, provide a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations.
1 code implementation • 22 Mar 2017 • Helena U. Zacharias, Thorsten Rehberg, Sebastian Mehrl, Daniel Richtmann, Tilo Wettig, Peter J. Oefner, Rainer Spang, Wolfram Gronwald, Michael Altenbuchinger
Motivation: Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum.