Least-squares methods for identifying biochemical regulatory networks from noisy measurements

@article{Kim2006LeastsquaresMF,
  title={Least-squares methods for identifying biochemical regulatory networks from noisy measurements},
  author={Jongrae Kim and Declan G. Bates and Ian Postlethwaite and Pat Heslop-Harrison and Kwang-Hyun Cho},
  journal={BMC Bioinformatics},
  year={2006},
  volume={8},
  pages={8 - 8}
}
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… CONTINUE READING

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