ACM Computing Surveys (CSUR)
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
Model gene network by semi-fixed Bayesian network
Expert Systems with Applications: An International Journal
IEEE Transactions on Signal Processing
State/noise estimator for descriptor systems with application to sensor fault diagnosis
IEEE Transactions on Signal Processing
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM
IEEE Transactions on Robotics
Predicting glaucomatous visual field deterioration through short multivariate time series modelling
Artificial Intelligence in Medicine
International Journal of Systems Science - Dynamics Analysis of Gene Regulatory Networks
Expert Systems with Applications: An International Journal
Intelligent data analysis: keeping pace with technological advances
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inferring Gene Regulatory Networks via Nonlinear State-Space Models and Exploiting Sparsity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
A Constrained Evolutionary Computation Method for Detecting Controlling Regions of Cortical Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Using gene expression programming to infer gene regulatory networks from time-series data
Computational Biology and Chemistry
Hi-index | 0.00 |
In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.