ISMB/ECCB 2023 - Poster

Using low-rank tensor formats to enable computations of cancer progression models in large state spaces

Simon Pfahler¹, Peter Georg¹, Y. Linda Hu², Stefan Vocht², Rudolf Schill^2,3, Andreas Lösch², Kevin Rupp^2,3, Stefan Hansch², Maren Klever⁴, Lars Grasedyck⁴, Rainer Spang², Tilo Wettig¹

¹ Department of Physics, University of Regensburg
² Department 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

Code availability

pRC: C++ library for efficient calculations in the Tensor Train format, open source

cMHN: C++ library to work with Mutual Hazard Networks in the Tensor Train format, open source

Funding

This work is supported by the German Research Foundation (DFG) through the project “Tensorapproximationsmethoden zur Modellierung von Tumorprogression”.

Simulation results

The data used to plot the KL-divergences and runtimes can be accessed here:
Runtime plot:

• non-TT
• TT - RP=16, RQ=8
• TT - RP=32, RQ=8
• TT - RP=32, RQ=16

Accuracy plot:

• TT - RP=16, RQ=8
• TT - RP=32, RQ=8
• TT - RP=32, RQ=16