Constrained Reduction Mapping for a Class of Network Models of Genomic Regulation
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abstract
Constructing network models of genomic regulation from data can help to better understand the manner in which genes interact in an integrative and holistic way within a given genome. One of the major impediments for the practical application of such models is their structural and computational complexity. Thus, it is sometimes necessary to construct computationally tractable sub-networks while still carrying sufficient structure for the application at hand. Hence, there is a need for size reducing mappings. This paper focuses on constrained reduction mappings for a particular class of network models that are inferred from non-temporal data. The constraints arise naturally from the structural and dynamical properties of the considered models. 2008 IEEE.
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2007 IEEE/NIH Life Science Systems and Applications Workshop