Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks | Zendy

Ilya Shmulevich | Zendy; Edward R. Dougherty | Zendy; Seungchan Kim | Zendy; Wei Zhang | Zendy

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Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks

Author(s) -

Ilya Shmulevich,

Edward R. Dougherty,

Seungchan Kim,

Wei Zhang

Publication year - 2002

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/18.2.261

Subject(s) - probabilistic logic , bayesian network , computer science , graphical model , gene regulatory network , context (archaeology) , markov chain , selection (genetic algorithm) , biological network , theoretical computer science , bayesian probability , class (philosophy) , artificial intelligence , machine learning , gene , computational biology , biology , genetics , paleontology , gene expression

Our goal is to construct a model for genetic regulatory networks such that the model class: (i) incorporates rule-based dependencies between genes; (ii) allows the systematic study of global network dynamics; (iii) is able to cope with uncertainty, both in the data and the model selection; and (iv) permits the quantification of the relative influence and sensitivity of genes in their interactions with other genes.

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