Exploiting weak modularity in cancer progression to infer large Mutual Hazard Networks

Simon Pfahler1, Leon Ernstberger1, Peter Georg1, Andreas Lösch2, Rudolf Schill3, Lars Grasedyck4, Rainer Spang2, Tilo Wettig1

1 Department of Physics, University of Regensburg
2 Faculty of Informatics and Data Science, University of Regensburg
3 Department of Biosystems Science and Engineering, ETH Zürich
4 Institute for Geometry and Applied Mathematics, RWTH Aachen University

Correspondence: simon.pfahler@ur.de

Poster

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Code availability

Code to reproduce the data presented on the poster is available in this GitHub repository, which uses the python packages fastmhn and mhn.

Funding

This work is supported by the German Research Foundation (DFG) through the project "Tensorapproximationsmethoden zur Modellierung von Tumorprogression", by the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes) and by the Marianne-Plehn-Programme of Bavaria.