ArticleBiometrics2014
A sparse Ising model with covariates.
Article in Biometrics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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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
24 citing papers in PubMed.
- Protein networks are influenced by maternal BMI and differentiate preterm birth types.Communications medicine · 2026Article
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- Bayesian estimation of covariate assisted principal regression for brain functional connectivity.Biostatistics (Oxford, England) · 2025Article
- Estimating personal temporal symptom networks for childhood cancer survivors.Communications medicine · 2025Article
- Jointly estimating individual and group networks from fMRI data.Network neuroscience (Cambridge, Mass.) · 2025Article
- Multi-task Learning for Gaussian Graphical Regressions with High Dimensional Covariates.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data.Journal of the American Statistical Association · 2024Article
- Low-rank regression models for multiple binary responses and their applications to cancer cell-line encyclopedia data.Journal of the American Statistical Association · 2024Article
- Estimation of Personal Symptom Networks Using the Ising Model for Adult Survivors of Childhood Cancer: A Simulation Study with Real-World Data Application.Clinical epidemiology · 2024Article
- Polymicrobial Foot Infection Patterns Are Common and Associated With Treatment Failure.Open forum infectious diseases · 2022Article
- Information enhanced model selection for Gaussian graphical model with application to metabolomic data.Biostatistics (Oxford, England) · 2022Article
- Conditional Gaussian graphical model for estimating personalized disease symptom networks.Statistics in medicine · 2022Article
- Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer.Journal of the American Statistical Association · 2022Article
- Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure.Journal of machine learning research : JMLR · 2022Article
- Bayesian graphical models for modern biological applications.Statistical methods & applications · 2022Review
- Identifying disease-associated biomarker network features through conditional graphical model.Biometrics · 2020Article
- Parasite associations predict infection risk: incorporating co-infections in predictive models for neglected tropical diseases.Parasites & vectors · 2020Article
- Identification of microbial interaction network: zero-inflated latent Ising model based approach.BioData mining · 2020Article
- An Integrated Gaussian Graphical Model to evaluate the impact of exposures on metabolic networks.Computers in biology and medicine · 2019Article
- Bayesian Graphical Regression.Journal of the American Statistical Association · 2019Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additional covariates are frequently recorded together with the binary data, and may influence the dependence relationships. Motivated by such a dataset on genomic instability collected from tumor samples of several types, we propose a sparse covariate dependent Ising model to study both the conditional dependency within the binary data and its relationship with the additional covariates. This results in subject-specific Ising models, where the subject's covariates influence the strength of association between the genes. As in all exploratory data analysis, interpretability of results is important, and we use ℓ1 penalties to induce sparsity in the fitted graphs and in the number of selected covariates. Two algorithms to fit the model are proposed and compared on a set of simulated data, and asymptotic results are established. The results on the tumor dataset and their biological significance are discussed in detail.
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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.