Delay-dependent exponential stability for a class of neural networks with time delays
Journal of Computational and Applied Mathematics
Improved global exponential stability criteria of cellular neural networks with time-varying delays
Mathematical and Computer Modelling: An International Journal
Brief Communication: Identifying the target mRNAs of microRNAs in colorectal cancer
Computational Biology and Chemistry
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Inferring Nonstationary Gene Networks from Longitudinal Gene Expression Microarrays
Journal of Signal Processing Systems
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Robust Bayesian Clustering for Replicated Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Ordinary differential equations (ODE) have been widely used for modeling and analysis of dynamic gene networks in systems biology. In this paper, we propose an optimization method that can infer a gene regulatory network from time-series gene expression data. Specifically, the following four cases are considered: (1) reconstruction of a gene network from synthetic gene expression data with noise, (2) reconstruction of a gene network from synthetic gene expression data with time-delay, (3) reconstruction of a gene network from synthetic gene expression data with noise and time-delay, and (4) reconstruction of a gene network from experimental time-series data in budding yeast cell cycle.