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Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/26941

Title: Least-squares methods for identifying biochemical regulatory networks from noisy measurements.
Authors: Kim, J
Bates, DG
Postlethwaite, I
Heslop-Harrison, P
Cho, KH
Issue Date: 2007
Citation: BMC BIOINFORMATICS, 2007, 8, pp. 8-8
Abstract: We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS) estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS). The Total Least Squares (TLS) technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks.
DOI Link: 10.1186/1471-2105-8-8
eISSN: 1471-2105
Links: http://hdl.handle.net/2381/26941
Type: Journal Article
Appears in Collections:Published Articles, Dept. of Biology

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