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Nonlinear bionetwork structure inference using the random sampling-high dimensional model representation (RS-HDMR) algorithm

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4 Author(s)
Miller, M. ; Dept. of Biol. Eng., Massachusetts Inst. of Technol., Cambridge, MA, USA ; Xiaojiang Feng ; Genyuan Li ; Rabitz, H.

This work presents the random sampling - high dimensional model representation (RS-HDMR) algorithm for identifying complex bionetwork structures from multivariate data. RS-HDMR describes network interactions through a hierarchy of input-output (IO) functions of increasing dimensionality. Sensitivity analysis based on the calculated RS-HDMR component functions provides a statistically interpretable measure of network interaction strength, and can be used to efficiently infer network structure. Advantages of RS-HDMR include the ability to capture nonlinear and cooperative realtionships among network components, the ability to handle both continuous and discrete relationships, the ability to be used as a high-dimensional IO model for quantitative property prediction, and favorable scalability with respect to the number of variables. To demonstrate, RS-HDMR was applied to experimental data measuring the single-cell response of a protein-protein signaling network to various perturbations. The resultant analysis identified the network structure comparable to that reported in the literature and to the results from a previous Bayesian network (BN) analysis. The IO model also revealed several nonlinear feedback and cooperative mechanisms that were unidentified through BN analysis.

Published in:

Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE

Date of Conference:

3-6 Sept. 2009