English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Probabilistic Boolean Networks have been proposed for estimating thebehaviour of dynamical systems as they combine rule-based modelling withuncertainty principles. Inferring PBNs directly from gene data is challenginghowever, especially when data is costly to collect and/or noisy, e.g., in thecase of gene expression profile data. In this paper, we present a reproduciblemethod for inferring PBNs directly from real gene expression data measurementstaken when the system was at a steady state. The steady-state dynamics of PBNsis of special interest in the analysis of biological machinery. The proposedapproach does not rely on reconstructing the state evolution of the network,which is computationally intractable for larger networks. We demonstrate themethod on samples of real gene expression profiling data from a well-knownstudy on metastatic melanoma. The pipeline is implemented using Python and wemake it publicly available.

Inferring probabilistic Boolean networks from steady-state gene data samples