A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks | Zendy

Xiaobo Zhou | Zendy; Xiaodong Wang | Zendy; Ranadip Pal | Zendy; Ivan Ivanov | Zendy; Michael Bittner | Zendy; Edward R. Dougherty | Zendy

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A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks

Author(s) -

Xiaobo Zhou,

Xiaodong Wang,

Ranadip Pal,

Ivan Ivanov,

Michael Bittner,

Edward R. Dougherty

Publication year - 2004

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/bth318

Subject(s) - gene regulatory network , biological network , computer science , markov chain monte carlo , probabilistic logic , bayesian network , reversible jump markov chain monte carlo , bayesian probability , markov chain , set (abstract data type) , attractor , dynamic bayesian network , artificial intelligence , machine learning , mathematics , computational biology , biology , gene , genetics , mathematical analysis , gene expression , programming language

We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes.

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