# 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",
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

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