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Detection of markers for discrete phenotypes

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Cifuentes Fontanals,  Laura       
IMPRS for Biology and Computation (Anne-Dominique Gindrat), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society;

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CSBio2021_Klarner et al_2021.pdf
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Citation

Klarner, H., Tonello, E., Cifuentes Fontanals, L., Janody, F., Chaouiya, C., & Siebert, H. (2021). Detection of markers for discrete phenotypes. In CSBio2021 (pp. 64-68). New York, NY: Association for Computing Machinery. doi:10.1145/3486713.3486729.


Cite as: https://hdl.handle.net/21.11116/0000-000E-57E7-C
Abstract
Motivation: Capturing the molecular diversity of living cells is not straightforward. One approach is to measure molecular markers that serve as indicators of specific biological conditions or phenotypes. This is particularly relevant in modern medicine to provide precise diagnostics and pinpoint the best treatment for each patient. The challenge is to select a minimal set of markers whose activity patterns are in correspondence with the phenotypes of interest. Results: This article approaches the marker detection problem in the context of discrete phenotypes which arise, for example, from Boolean models of cellular networks. Mathematically this poses a combinatorial optimization problem with many answers. We propose a solution to this optimization problem that is based on the modelling language answer set programming (ASP). A case study of a death cell receptor network illustrates the methodology. Discussion and code: For code, discussions and reporting errors visit https://github.com/hklarner/detection_of_markers_for_discrete_phenotypes.