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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kimura, S. Matsumura, K. Okada-Hatakeyama, M. |
| Copyright Year | 2012 |
| Description | Author affiliation: Graduate School of Engineering, Tottori University, JAPAN (Kimura, S.; Matsumura, K.) || RIKEN RCAI, Yokohama, JAPAN (Okada-Hatakeyama, M.) |
| Abstract | For the inference of S-system models of genetic networks, this study proposes a new method, i.e., a two-phase estimation method. The two-phase estimation method is an extension of the decoupling approach proposed by Voit and Almeida. The decoupling approach defines the estimation of S-system parameters as a problem of solving sets of non-linear algebraic equations. Our method first transforms each set of non-linear algebraic equations, that is defined by the decoupling approach, into a set of linear ones. The transformation of the equations is easily accomplished by solving a linear programming problem. The proposed method then estimates S-system parameters by solving the transformed linear equations. As the proposed two-phase estimation method infers an S-system model only by solving linear programming problems and sets of linear algebraic equations, it always provides us with a unique solution. Moreover, its computational cost is very low. Finally, we confirm the effectiveness of the proposed method through numerical experiments. |
| Starting Page | 1 |
| Ending Page | 8 |
| File Size | 780625 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781467314886 |
| ISSN | 21614393 |
| e-ISBN | 9781467314909 |
| e-ISBN | 9781467314893 |
| DOI | 10.1109/IJCNN.2012.6252644 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Equations Mathematical model Estimation Gene expression Training Linear programming LPM Genetic network S-system |
| Content Type | Text |
| Resource Type | Article |
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