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Erschienen in:
Cognitive Cascades within Media Ecosystems: Simulating Fragmentation, Selective Exposure and Media Tactics to Investigate Polarization Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Inferring Probabilistic Boolean Networks from Steady-State Gene Data Samples

verfasst von : Vytenis Šliogeris, Leandros Maglaras, Sotiris Moschoyiannis

Erschienen in: Complex Networks and Their Applications XI

Verlag: Springer International Publishing

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Abstract

Probabilistic Boolean Networks have been proposed for estimating the behaviour of dynamical systems as they combine rule-based modelling with uncertainty principles. Inferring PBNs directly from gene data is challenging however, especially when data is costly to collect and/or noisy, e.g., in the case of gene expression profile data. In this paper, we present a reproducible method for inferring PBNs directly from real gene expression data measurements taken when the system was at a steady state. The steady-state dynamics of PBNs is of special interest in the analysis of biological machinery. The proposed approach does not rely on reconstructing the state evolution of the network, which is computationally intractable for larger networks. We demonstrate the method on samples of real gene expression profiling data from a well-known study on metastatic melanoma. The pipeline is implemented using Python and we make it publicly available.

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Vorheriges Kapitel Building Differential Co-expression Networks with Variable Selection and Regularization
Nächstes Kapitel Quantifying High-Order Interactions in Complex Physiological Networks: A Frequency-Specific Approach
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Metadaten
Titel
Inferring Probabilistic Boolean Networks from Steady-State Gene Data Samples
verfasst von
Vytenis Šliogeris
Leandros Maglaras
Sotiris Moschoyiannis
Copyright-Jahr
2023
Buch
Complex Networks and Their Applications XI

Print ISBN: 978-3-031-21126-3

Electronic ISBN: 978-3-031-21127-0

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-3-031-21127-0_24

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