Modeling Genetic Regulatory Networks by Sigmoidal Functions: A Joint Genetic Algorithm and Kalman Filtering Approach

2007 ◽  
Author(s):  
Haixin Wang ◽  
Lijun Qian ◽  
Edward Dougherty
Keyword(s):  
Genetic Algorithm ◽  
Kalman Filtering ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Sigmoidal Functions ◽  
Filtering Approach

scholarly journals Estimating Uncertain Delayed Genetic Regulatory Networks: An Adaptive Filtering Approach

2009 ◽  
Vol 54 (4) ◽  
pp. 892-897 ◽  
Author(s):  
Wenwu Yu ◽  
Jinhu Lu ◽  
Guanrong Chen ◽  
Zhisheng Duan ◽  
Qianhe Zhou
Keyword(s):  
Adaptive Filtering ◽  
Regulatory Networks ◽  
Genetic Regulatory Networks ◽  
Filtering Approach

scholarly journals Kalman Filtering for Genetic Regulatory Networks with Missing Values

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Qiongbin Lin ◽  
Qiuhua Liu ◽  
Tianyue Lai ◽  
Wu Wang
Keyword(s):  
Kalman Filtering ◽  
Time Delays ◽  
Regulatory Networks ◽  
Missing Values ◽  
Genetic Regulation ◽  
Genetic Regulatory Networks ◽  
Measurement Noise ◽  
Process Noise ◽  
Missing Value ◽  
Measurement Equations

The filter problem with missing value for genetic regulation networks (GRNs) is addressed, in which the noises exist in both the state dynamics and measurement equations; furthermore, the correlation between process noise and measurement noise is also taken into consideration. In order to deal with the filter problem, a class of discrete-time GRNs with missing value, noise correlation, and time delays is established. Then a new observation model is proposed to decrease the adverse effect caused by the missing value and to decouple the correlation between process noise and measurement noise in theory. Finally, a Kalman filtering is used to estimate the states of GRNs. Meanwhile, a typical example is provided to verify the effectiveness of the proposed method, and it turns out to be the case that the concentrations of mRNA and protein could be estimated accurately.

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