Published November 23, 2022
| Version v2
Journal article
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Multi-Omics Regulatory Network Inference in the Presence of Missing Data
Creators
- 1. Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL)
- 2. Chair of Experimental Bioinformatics, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, 85354 Freising
- 3. Helmholtz Zentrum München, Computational Health Department, Ingolstädter Landstraße 1, 85764 Munich, Germany, Member of the German Center for Lung Research (DZL
Description
Multiomics Regulatory Network Inference in the Presence of Missing Data
We present a benchmarking of six different lasso models integrated within the KiMONo approach looking for network inference using multi-omics data dealing with missing information.
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