A posterior probability approach for gene regulatory network inference in genetic perturbation data
Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring...
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| Format: | Article |
| Language: | English |
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AIMS Press
2016-07-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2016041 |
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| author | William Chad Young Adrian E. Raftery Ka Yee Yeung |
| author_facet | William Chad Young Adrian E. Raftery Ka Yee Yeung |
| author_sort | William Chad Young |
| collection | DOAJ |
| description | Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach. |
| format | Article |
| id | doaj-art-4493cd9a123d4e839051c9d44cc3d49f |
| institution | Kabale University |
| issn | 1551-0018 |
| language | English |
| publishDate | 2016-07-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj-art-4493cd9a123d4e839051c9d44cc3d49f2025-01-24T02:37:49ZengAIMS PressMathematical Biosciences and Engineering1551-00182016-07-011361241125110.3934/mbe.2016041A posterior probability approach for gene regulatory network inference in genetic perturbation dataWilliam Chad Young0Adrian E. Raftery1Ka Yee Yeung2University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195-4322University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195-4322University of Washington, Institute of Technology, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach.https://www.aimspress.com/article/doi/10.3934/mbe.2016041statisticsgene regulatory networkstatistical computation.bayesian analysis |
| spellingShingle | William Chad Young Adrian E. Raftery Ka Yee Yeung A posterior probability approach for gene regulatory network inference in genetic perturbation data Mathematical Biosciences and Engineering statistics gene regulatory network statistical computation. bayesian analysis |
| title | A posterior probability approach for gene regulatory network inference in genetic perturbation data |
| title_full | A posterior probability approach for gene regulatory network inference in genetic perturbation data |
| title_fullStr | A posterior probability approach for gene regulatory network inference in genetic perturbation data |
| title_full_unstemmed | A posterior probability approach for gene regulatory network inference in genetic perturbation data |
| title_short | A posterior probability approach for gene regulatory network inference in genetic perturbation data |
| title_sort | posterior probability approach for gene regulatory network inference in genetic perturbation data |
| topic | statistics gene regulatory network statistical computation. bayesian analysis |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2016041 |
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