ArticleBiometrics2015
Bayesian nonlinear model selection for gene regulatory networks.
Article in Biometrics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed.
- BAYESIAN DIFFERENTIAL CAUSAL DIRECTED ACYCLIC GRAPHS FOR OBSERVATIONAL ZERO-INFLATED COUNTS WITH AN APPLICATION TO TWO-SAMPLE SINGLE-CELL DATA.The annals of applied statistics · 2025Article
- A Selective Review of Network Analysis Methods for Gene Expression Data.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Tumor radiogenomics in gliomas with Bayesian layered variable selection.Medical image analysis · 2023Article
- Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer.Biometrics · 2023Article
- Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Bayesian graphical models for modern biological applications.Statistical methods & applications · 2022Review
- Probabilistic cause-of-disease assignment using case-control diagnostic tests: A latent variable regression approach.Statistics in medicine · 2021Article
- Estimating Linear and Nonlinear Gene Coexpression Networks by Semiparametric Neighborhood Selection.Genetics · 2020Article
- TGCnA: temporal gene coexpression network analysis using a low-rank plus sparse framework.Journal of applied statistics · 2020Article
- Bayesian Graphical Regression.Journal of the American Statistical Association · 2019Article
- Personalized Integrated Network Modeling of the Cancer Proteome Atlas.Scientific reports · 2018Article
- Bayesian graphical models for computational network biology.BMC bioinformatics · 2018Article
- Statistical Contributions to Bioinformatics: Design, Modeling, Structure Learning, and Integration.Statistical modelling · 2017Article
- Inference of Gene Regulatory Networks Using Bayesian Nonparametric Regression and Topology Information.Computational and mathematical methods in medicine · 2017Article
- Integrative bayesian network analysis of genomic data.Cancer informatics · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Gene regulatory networks represent the regulatory relationships between genes and their products and are important for exploring and defining the underlying biological processes of cellular systems. We develop a novel framework to recover the structure of nonlinear gene regulatory networks using semiparametric spline-based directed acyclic graphical models. Our use of splines allows the model to have both flexibility in capturing nonlinear dependencies as well as control of overfitting via shrinkage, using mixed model representations of penalized splines. We propose a novel discrete mixture prior on the smoothing parameter of the splines that allows for simultaneous selection of both linear and nonlinear functional relationships as well as inducing sparsity in the edge selection. Using simulation studies, we demonstrate the superior performance of our methods in comparison with several existing approaches in terms of network reconstruction and functional selection. We apply our methods to a gene expression dataset in glioblastoma multiforme, which reveals several interesting and biologically relevant nonlinear relationships.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.