Identification of metabolic network models from incomplete high-throughput datasets | Zendy

Sara Berthoumieux | Zendy; Matteo Brilli | Zendy; Hidde de Jong | Zendy; Daniel Kahn | Zendy; Eugenio Cinquemani | Zendy

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Identification of metabolic network models from incomplete high-throughput datasets

Author(s) -

Sara Berthoumieux,

Matteo Brilli,

Hidde de Jong,

Daniel Kahn,

Eugenio Cinquemani

Publication year - 2011

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/btr225

Subject(s) - identification (biology) , computer science , context (archaeology) , missing data , expectation–maximization algorithm , data mining , variety (cybernetics) , maximization , throughput , data integration , machine learning , artificial intelligence , maximum likelihood , mathematical optimization , mathematics , statistics , biology , paleontology , telecommunications , botany , wireless

High-throughput measurement techniques for metabolism and gene expression provide a wealth of information for the identification of metabolic network models. Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance.

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