A probabilistic graphical model for system-wide analysis of gene regulatory networks | Zendy

Stephen Kotiang | Zendy; Ali Eslami | Zendy

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A probabilistic graphical model for system-wide analysis of gene regulatory networks

Author(s) -

Stephen Kotiang,

Ali Eslami

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa122

Subject(s) - computer science , graphical model , gene regulatory network , probabilistic logic , data mining , consistency (knowledge bases) , systems biology , inference , software , bipartite graph , statistical model , graph , theoretical computer science , computational biology , machine learning , artificial intelligence , biology , gene , gene expression , biochemistry , programming language

The inference of gene regulatory networks (GRNs) from DNA microarray measurements forms a core element of systems biology-based phenotyping. In the recent past, numerous computational methodologies have been formalized to enable the deduction of reliable and testable predictions in today's biology. However, little focus has been aimed at quantifying how well existing state-of-the-art GRNs correspond to measured gene-expression profiles.

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