ArticleComputational and mathematical methods in medicine2017
Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information.
Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A new Bayesian piecewise linear regression model for dynamic network reconstruction.BMC bioinformatics · 2021Article
- Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization.Computational and mathematical methods in medicine · 2021Article
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3 authors.
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Abstract
Gene regulatory networks (GRNs) play an important role in cellular systems and are important for understanding biological processes. Many algorithms have been developed to infer the GRNs. However, most algorithms only pay attention to the gene expression data but do not consider the topology information in their inference process, while incorporating this information can partially compensate for the lack of reliable expression data. Here we develop a Bayesian group lasso with spike and slab priors to perform gene selection and estimation for nonparametric models. B-spline basis functions are used to capture the nonlinear relationships flexibly and penalties are used to avoid overfitting. Further, we incorporate the topology information into the Bayesian method as a prior. We present the application of our method on DREAM3 and DREAM4 datasets and two real biological datasets. The results show that our method performs better than existing methods and the topology information prior can improve the result.
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