文章基本信息

标题：Reverse Engineering Sparse Gene Regulatory Networks Using Cubature Kalman Filter and Compressed Sensing
本地全文：下载
作者：Amina Noor ; Erchin Serpedin ; Mohamed Nounou 等
期刊名称：Advances in Bioinformatics
印刷版ISSN：1687-8027
电子版ISSN：1687-8035
出版年度：2013
卷号：2013
DOI：10.1155/2013/205763
出版社：Hindawi Publishing Corporation
摘要：This paper proposes a novel algorithm for inferring gene regulatory networks which makes use of cubature Kalman filter (CKF) and Kalman filter (KF) techniques in conjunction with compressed sensing methods. The gene network is described using a state-space model. A nonlinear model for the evolution of gene expression is considered, while the gene expression data is assumed to follow a linear Gaussian model. The hidden states are estimated using CKF. The system parameters are modeled as a Gauss-Markov process and are estimated using compressed sensing-based KF. These parameters provide insight into the regulatory relations among the genes. The Cramér-Rao lower bound of the parameter estimates is calculated for the system model and used as a benchmark to assess the estimation accuracy. The proposed algorithm is evaluated rigorously using synthetic data in different scenarios which include different number of genes and varying number of sample points. In addition, the algorithm is tested on the DREAM4 in silico data sets as well as the in vivo data sets from IRMA network. The proposed algorithm shows superior performance in terms of accuracy, robustness, and scalability.