Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
System Identification through Simulated Evolution: A Machine Learning Approach to Modeling
Evolutionary modeling and inference of gene network
Information Sciences—Informatics and Computer Science: An International Journal - Bioinformatics-selected papers from 4th CBGI & 6th JCIS Proceedings
Finding Multimodal Solutions Using Restricted Tournament Selection
Proceedings of the 6th International Conference on Genetic Algorithms
Parameter Estimation Using Metaheuristics in Systems Biology: A Comprehensive Review
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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With the availability of the time series data from the high-throughput technologies, diverse approaches have been proposed to model gene regulatory networks. Compared with others, S-system has the advantage for these tasks in the sense that it can provide both quantitative (structural) and qualitative (dynamical) modeling in one framework. However, it is not easy to identify the structure of the true network since the number of parameters to be estimated is much larger than that of the available data. Moreover, conventional parameter estimation requires the time-consuming numerical integration to reproduce dynamic profiles for the S-system. In this paper, we propose multi-stage evolutionary algorithms to identify gene regulatory networks efficiently. With the symbolic regression by genetic programming (GP), we can evade the numerical integration steps. This is because the estimation of slopes for each time-course data can be obtained from the results of GP. We also develop hybrid evolutionary algorithms and modified fitness evaluation function to identify the structure of gene regulatory networks and to estimate the corresponding parameters at the same time. By applying the proposed method to the identification of an artificial genetic network, we verify its capability of finding the true S-system.