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  Detection of markers for discrete phenotypes

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.

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CSBio2021_Klarner et al_2021.pdf (Publisher version), 673KB
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CSBio2021_Klarner et al_2021.pdf
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 Creators:
Klarner, Hannes , Author
Tonello, Elisa , Author
Cifuentes Fontanals, Laura1, Author                 
Janody, Florence , Author
Chaouiya, Claudine , Author
Siebert, Heike, Author
Affiliations:
1IMPRS for Biology and Computation (Anne-Dominique Gindrat), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479666              

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Free keywords: Marker Detection, Discrete Phenotypes, Biological Networks
 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.

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Language(s): eng - English
 Dates: 2021-12-14
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1145/3486713.3486729
 Degree: -

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Title: CSBio2021: The 12th International Conference on Computational Systems-Biology and Bioinformatics
Place of Event: Virtual Event
Start-/End Date: 2021-10-14 - 2021-10-15

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Title: CSBio2021
Source Genre: Proceedings
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Publ. Info: New York, NY : Association for Computing Machinery
Pages: 5 Volume / Issue: - Sequence Number: - Start / End Page: 64 - 68 Identifier: ISBN: 978-1-4503-8510-7