Probabilistic edge inference of gene networks with markov random field-based bayesian learning
Current algorithms for gene regulatory network construction based on Gaussian graphical models focuses on the deterministic decision of whether an edge exists. Both the probabilistic inference of edge existence and the relative strength of edges are often overlooked, either because the computational...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-11-01
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| Series: | Frontiers in Genetics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2022.1034946/full |
| _version_ | 1811232634116767744 |
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| author | Yu-Jyun Huang Rajarshi Mukherjee Chuhsing Kate Hsiao Chuhsing Kate Hsiao |
| author_facet | Yu-Jyun Huang Rajarshi Mukherjee Chuhsing Kate Hsiao Chuhsing Kate Hsiao |
| author_sort | Yu-Jyun Huang |
| collection | DOAJ |
| description | Current algorithms for gene regulatory network construction based on Gaussian graphical models focuses on the deterministic decision of whether an edge exists. Both the probabilistic inference of edge existence and the relative strength of edges are often overlooked, either because the computational algorithms cannot account for this uncertainty or because it is not straightforward in implementation. In this study, we combine the Bayesian Markov random field and the conditional autoregressive (CAR) model to tackle simultaneously these two tasks. The uncertainty of edge existence and the relative strength of edges can be measured and quantified based on a Bayesian model such as the CAR model and the spike-and-slab lasso prior. In addition, the strength of the edges can be utilized to prioritize the importance of the edges in a network graph. Simulations and a glioblastoma cancer study were carried out to assess the proposed model’s performance and to compare it with existing methods when a binary decision is of interest. The proposed approach shows stable performance and may provide novel structures with biological insights. |
| first_indexed | 2024-04-12T11:07:25Z |
| format | Article |
| id | doaj.art-eafac532acf74b5388e20ec7a0c68a80 |
| institution | Directory Open Access Journal |
| issn | 1664-8021 |
| language | English |
| last_indexed | 2024-04-12T11:07:25Z |
| publishDate | 2022-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Genetics |
| spelling | doaj.art-eafac532acf74b5388e20ec7a0c68a802022-12-22T03:35:44ZengFrontiers Media S.A.Frontiers in Genetics1664-80212022-11-011310.3389/fgene.2022.10349461034946Probabilistic edge inference of gene networks with markov random field-based bayesian learningYu-Jyun Huang0Rajarshi Mukherjee1Chuhsing Kate Hsiao2Chuhsing Kate Hsiao3Division of Biostatistics and Data Science, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, TaiwanDepartment of Biostatistics, Harvard University, Boston, MA, United StatesDivision of Biostatistics and Data Science, Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, TaiwanBioinformatics and Biostatistics Core, Center of Genomic Medicine, National Taiwan University, Taipei, TaiwanCurrent algorithms for gene regulatory network construction based on Gaussian graphical models focuses on the deterministic decision of whether an edge exists. Both the probabilistic inference of edge existence and the relative strength of edges are often overlooked, either because the computational algorithms cannot account for this uncertainty or because it is not straightforward in implementation. In this study, we combine the Bayesian Markov random field and the conditional autoregressive (CAR) model to tackle simultaneously these two tasks. The uncertainty of edge existence and the relative strength of edges can be measured and quantified based on a Bayesian model such as the CAR model and the spike-and-slab lasso prior. In addition, the strength of the edges can be utilized to prioritize the importance of the edges in a network graph. Simulations and a glioblastoma cancer study were carried out to assess the proposed model’s performance and to compare it with existing methods when a binary decision is of interest. The proposed approach shows stable performance and may provide novel structures with biological insights.https://www.frontiersin.org/articles/10.3389/fgene.2022.1034946/fullBayesian markov random fieldedge prioritizationexistence probabilitygene regulatory networknetwork structureprobabilistic association |
| spellingShingle | Yu-Jyun Huang Rajarshi Mukherjee Chuhsing Kate Hsiao Chuhsing Kate Hsiao Probabilistic edge inference of gene networks with markov random field-based bayesian learning Frontiers in Genetics Bayesian markov random field edge prioritization existence probability gene regulatory network network structure probabilistic association |
| title | Probabilistic edge inference of gene networks with markov random field-based bayesian learning |
| title_full | Probabilistic edge inference of gene networks with markov random field-based bayesian learning |
| title_fullStr | Probabilistic edge inference of gene networks with markov random field-based bayesian learning |
| title_full_unstemmed | Probabilistic edge inference of gene networks with markov random field-based bayesian learning |
| title_short | Probabilistic edge inference of gene networks with markov random field-based bayesian learning |
| title_sort | probabilistic edge inference of gene networks with markov random field based bayesian learning |
| topic | Bayesian markov random field edge prioritization existence probability gene regulatory network network structure probabilistic association |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2022.1034946/full |
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