Inference of Genetic Regulatory Networks by Evolutionary Algorithm and $H_\infty$ Filtering

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@InProceedings{Qian:2007:SSP,

• author = "Lijun Qian and Haixin Wang",
• title = "Inference of Genetic Regulatory Networks by Evolutionary Algorithm and {$H{_\infty}$} Filtering",
• booktitle = "14th IEEE/SP Workshop on Statistical Signal Processing, SSP '07",
• year = "2007",
• pages = "21--25",
• address = "Madison, WI, USA",
• month = "26-29 " # aug,
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-4244-1198-6",
• URL = "http://old.pvamu.edu/edir/lijun/files/papers/SSP2007.pdf",
• DOI = "doi:10.1109/SSP.2007.4301210",
• size = "5 pages",
• abstract = "The correct inference of genetic regulatory networks plays a critical role in understanding biological regulation in phenotypic determination and it can affect advanced genome-based therapeutics. In this study, we propose a joint evolutionary algorithm and Hinfinity filtering approach to infer genetic regulatory networks using noisy time series data from microarray measurements. Specifically, an iterative algorithm is proposed where genetic programming is applied to identify the structure of the model and H infinity filtering is used to estimate the parameters in each iteration. The proposed method can obtain accurate dynamic nonlinear ordinary differential equation (ODE) model of genetic regulatory networks even when the noise statistics is unknown. Both synthetic data and experimental data from microarray measurements are used to demonstrate the effectiveness of the proposed method. With the increasing availability of time series microarray data, the algorithm developed in this paper could be applied to construct models to characterise cancer evolution and serve as the basis for developing new regulatory therapies.",
• notes = "Department of Electrical and Computer Engineering, Prairie View A&M University, Prairie View, Texas 77446.",

}

Genetic Programming entries for Lijun Qian Haixin Wang

Citations