Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data | Zendy

Jongrae Kim | Zendy; Declan G. Bates | Zendy; Ian Postlethwaite | Zendy; J. S. HeslopHarrison | Zendy; KwangHyun Cho | Zendy

Open Access

Linear time-varying models can reveal non-linear interactions of biomolecular regulatory networks using multiple time-series data

Author(s) -

Jongrae Kim,

Declan G. Bates,

Ian Postlethwaite,

J. S. HeslopHarrison,

KwangHyun Cho

Publication year - 2008

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/btn107

Subject(s) - computer science , time series , inference , linear model , lti system theory , series (stratigraphy) , algorithm , mathematical optimization , linear system , artificial intelligence , mathematics , machine learning , paleontology , biology , mathematical analysis

Inherent non-linearities in biomolecular interactions make the identification of network interactions difficult. One of the principal problems is that all methods based on the use of linear time-invariant models will have fundamental limitations in their capability to infer certain non-linear network interactions. Another difficulty is the multiplicity of possible solutions, since, for a given dataset, there may be many different possible networks which generate the same time-series expression profiles.

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