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Inference of S-system Models of Genetic Networks from Noisy Time-series Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Kimura, Shuhei Hatakeyama, Mariko Konagaya, Akihiko |
| Copyright Year | 2004 |
| Abstract | In this paper, we propose a new method for the inference of S-system models of large-scale genetic networks from the observed time-series data of gene expression patterns. The proposed method employs a technique to decompose the genetic network inference problem into several subproblems. The S-system parameters are estimated by solving these decomposed subproblems. In addition, the proposed method estimates the initial levels of the gene expression. The estimation of the initial gene expression levels is necessary when the noisy time-series data are given. We verify the effectiveness of the proposed method through the genetic network inference problems. |
| Starting Page | 1 |
| Ending Page | 14 |
| Page Count | 14 |
| File Format | PDF HTM / HTML |
| DOI | 10.1273/cbij.4.1 |
| Alternate Webpage(s) | http://www.cbi.or.jp/cbi/CBIj/vol4/4_1-E.pdf |
| Alternate Webpage(s) | https://doi.org/10.1273/cbij.4.1 |
| Volume Number | 4 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
