Abstract
The extended Kalman filter (EKF) has been applied to inferring gene regulatory networks. However, it is well known that the EKF becomes less accurate when the system exhibits high nonlinearity. In addition, certain prior information about the gene regulatory network exists in practice, and no systematic approach has been developed to incorporate such prior information into the Kalman-type filter for inferring the structure of the gene regulatory network. In this paper, an inference framework based on point-based Gaussian approximation filters that can exploit the prior information is developed to solve the gene regulatory network inference problem. Different point-based Gaussian approximation filters, including the unscented Kalman filter (UKF), the third-degree cubature Kalman filter (CKF3), and the fifth-degree cubature Kalman filter (CKF5) are employed. Several types of network prior information, including the existing network structure information, sparsity assumption, and the range constraint of parameters, are considered, and the corresponding filters incorporating the prior information are developed. Experiments on a synthetic network of eight genes and the yeast protein synthesis network of five genes are carried out to demonstrate the performance of the proposed framework. The results show that the proposed methods provide more accurate inference results than existing methods, such as the EKF and the traditional UKF.
Highlights
Inferring gene regulatory network (GRN) has become one of the most important missions in system biology
Where the number of true positives (TP#) denotes the number of links correctly predicted by the inference algorithm; the number of false positives (FP#) denotes the number of incorrectly predicted links; the number of true negatives (TN#) denotes the number of correctly predicted nonlinks; and the number of false negatives (FN#) denotes the number of missed links by the inference algorithm [8]
It can be seen that all point-based Gaussian approximation filters have better performance than the extended Kalman filter (EKF) since the average(avg) false positive rate (FPR) is lower and the average true positive rate (TPR) and precision are higher than that of the EKF
Summary
Inferring gene regulatory network (GRN) has become one of the most important missions in system biology. Various approaches based on different models have been used to infer the network from observed gene expression data, such as the. Due to the ‘stochastic’ nature of the gene expression, the Kalman filtering approach based on the state-space model is one of the most competitive methods for inferring the GRN. We consider the point-based Gaussian approximation filters. Integration of the prior knowledge or constraints with the GRN inference algorithm has been introduced to improve the inference result. That cost function is not coupled well with the filtering algorithm It did not consider other kinds of prior information. We propose a new framework that incorporates the prior information effectively in the filtering algorithm by solving a constrained optimization problem.
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