Reconstruct gene regulatory network using slice pattern model
<p>Abstract</p> <p>Background</p> <p>Gene expression time series array data has become a useful resource for investigating gene functions and the interactions between genes. However, the gene expression arrays are always mixed with noise, and many nonlinear regulatory r...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2009-07-01
|
| Series: | BMC Genomics |
| _version_ | 1818526445754908672 |
|---|---|
| author | Yang Bo Wang Guohua Wang Yadong Tao Haijun Yang Jack Y Deng Youping Liu Yunlong |
| author_facet | Yang Bo Wang Guohua Wang Yadong Tao Haijun Yang Jack Y Deng Youping Liu Yunlong |
| author_sort | Yang Bo |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Gene expression time series array data has become a useful resource for investigating gene functions and the interactions between genes. However, the gene expression arrays are always mixed with noise, and many nonlinear regulatory relationships have been omitted in many linear models. Because of those practical limitations, inference of gene regulatory model from expression data is still far from satisfactory.</p> <p>Results</p> <p>In this study, we present a model-based computational approach, Slice Pattern Model (SPM), to identify gene regulatory network from time series gene expression array data. In order to estimate performances of stability and reliability of our model, an artificial gene network is tested by the traditional linear model and SPM. SPM can handle the multiple transcriptional time lags and more accurately reconstruct the gene network. Using SPM, a 17 time-series gene expression data in yeast cell cycle is retrieved to reconstruct the regulatory network. Under the reliability threshold, <it>θ </it>= 55%, 18 relationships between genes are identified and transcriptional regulatory network is reconstructed. Results from previous studies demonstrate that most of gene relationships identified by SPM are correct.</p> <p>Conclusion</p> <p>With the help of pattern recognition and similarity analysis, the effect of noise has been limited in SPM method. At the same time, genetic algorithm is introduced to optimize parameters of gene network model, which is performed based on a statistic method in our experiments. The results of experiments demonstrate that the gene regulatory model reconstructed using SPM is more stable and reliable than those models coming from traditional linear model.</p> |
| first_indexed | 2024-12-11T06:23:02Z |
| format | Article |
| id | doaj.art-6c12b607a45d4c5a9a3a32599aa5d0e3 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-11T06:23:02Z |
| publishDate | 2009-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-6c12b607a45d4c5a9a3a32599aa5d0e32022-12-22T01:17:46ZengBMCBMC Genomics1471-21642009-07-0110Suppl 1S210.1186/1471-2164-10-S1-S2Reconstruct gene regulatory network using slice pattern modelYang BoWang GuohuaWang YadongTao HaijunYang Jack YDeng YoupingLiu Yunlong<p>Abstract</p> <p>Background</p> <p>Gene expression time series array data has become a useful resource for investigating gene functions and the interactions between genes. However, the gene expression arrays are always mixed with noise, and many nonlinear regulatory relationships have been omitted in many linear models. Because of those practical limitations, inference of gene regulatory model from expression data is still far from satisfactory.</p> <p>Results</p> <p>In this study, we present a model-based computational approach, Slice Pattern Model (SPM), to identify gene regulatory network from time series gene expression array data. In order to estimate performances of stability and reliability of our model, an artificial gene network is tested by the traditional linear model and SPM. SPM can handle the multiple transcriptional time lags and more accurately reconstruct the gene network. Using SPM, a 17 time-series gene expression data in yeast cell cycle is retrieved to reconstruct the regulatory network. Under the reliability threshold, <it>θ </it>= 55%, 18 relationships between genes are identified and transcriptional regulatory network is reconstructed. Results from previous studies demonstrate that most of gene relationships identified by SPM are correct.</p> <p>Conclusion</p> <p>With the help of pattern recognition and similarity analysis, the effect of noise has been limited in SPM method. At the same time, genetic algorithm is introduced to optimize parameters of gene network model, which is performed based on a statistic method in our experiments. The results of experiments demonstrate that the gene regulatory model reconstructed using SPM is more stable and reliable than those models coming from traditional linear model.</p> |
| spellingShingle | Yang Bo Wang Guohua Wang Yadong Tao Haijun Yang Jack Y Deng Youping Liu Yunlong Reconstruct gene regulatory network using slice pattern model BMC Genomics |
| title | Reconstruct gene regulatory network using slice pattern model |
| title_full | Reconstruct gene regulatory network using slice pattern model |
| title_fullStr | Reconstruct gene regulatory network using slice pattern model |
| title_full_unstemmed | Reconstruct gene regulatory network using slice pattern model |
| title_short | Reconstruct gene regulatory network using slice pattern model |
| title_sort | reconstruct gene regulatory network using slice pattern model |
| work_keys_str_mv | AT yangbo reconstructgeneregulatorynetworkusingslicepatternmodel AT wangguohua reconstructgeneregulatorynetworkusingslicepatternmodel AT wangyadong reconstructgeneregulatorynetworkusingslicepatternmodel AT taohaijun reconstructgeneregulatorynetworkusingslicepatternmodel AT yangjacky reconstructgeneregulatorynetworkusingslicepatternmodel AT dengyouping reconstructgeneregulatorynetworkusingslicepatternmodel AT liuyunlong reconstructgeneregulatorynetworkusingslicepatternmodel |