ArticleJournal of machine learning research : JMLR2018
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models.
Article in Journal of machine learning research : JMLR, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Network-based hierarchical heterogeneity analysis and applications to cancer omics data.Science China. Mathematics · 2026Article
- Robust Heterogeneity Adjustment for Gaussian Graphical Model With Latent Variables.Statistics in medicine · 2026Article
- LtransHeteroGGM: local transfer learning for Gaussian graphical model-based heterogeneity analysis.Bioinformatics (Oxford, England) · 2026Article
- Incorporating prior information in gene expression network-based cancer heterogeneity analysis.Biostatistics (Oxford, England) · 2025Article
- Simultaneous clustering and estimation of networks in multiple graphical models.Biostatistics (Oxford, England) · 2025Article
- Subgroup Analysis of Differential Networks with Latent Variables.Statistics and computing · 2025Article
- A framework for analyzing EEG data using high-dimensional tests.Bioinformatics (Oxford, England) · 2025Article
- Estimation of multiple networks with common structures in heterogeneous subgroups.Journal of multivariate analysis · 2024Article
- Heterogeneous latent transfer learning in Gaussian graphical models.Biometrics · 2024Article
- Simultaneous cluster structure learning and estimation of heterogeneous graphs for matrix-variate fMRI data.Biometrics · 2023Article
- Penalized model-based clustering of fMRI data.Biostatistics (Oxford, England) · 2022Article
- Article
- Network-based cancer heterogeneity analysis incorporating multi-view of prior information.Bioinformatics (Oxford, England) · 2022Article
- Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer.Journal of the American Statistical Association · 2022Article
- INTEGRATIVE NETWORK LEARNING FOR MULTI-MODALITY BIOMARKER DATA.The annals of applied statistics · 2021Article
- Bayesian inference of networks across multiple sample groups and data types.Biostatistics (Oxford, England) · 2020Article
- Integrating approximate single factor graphical models.Statistics in medicine · 2020Article
- Bayesian Joint Spike-and-Slab Graphical Lasso.Proceedings of machine learning research · 2019Article
- Differential Network Analysis: A Statistical Perspective.Wiley interdisciplinary reviews. Computational statisticsArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We consider joint estimation of multiple graphical models arising from heterogeneous and high-dimensional observations. Unlike most previous approaches which assume that the cluster structure is given in advance, an appealing feature of our method is to learn cluster structure while estimating heterogeneous graphical models. This is achieved via a high dimensional version of Expectation Conditional Maximization (ECM) algorithm (Meng and Rubin, 1993). A joint graphical lasso penalty is imposed on the conditional maximization step to extract both homogeneity and heterogeneity components across all clusters. Our algorithm is computationally efficient due to fast sparse learning routines and can be implemented without unsupervised learning knowledge. The superior performance of our method is demonstrated by extensive experiments and its application to a Glioblastoma cancer dataset reveals some new insights in understanding the Glioblastoma cancer. In theory, a non-asymptotic error bound is established for the output directly from our high dimensional ECM algorithm, and it consists of two quantities:
Indexed as
Identifiers
30662373PMC6338433What OpenQuestion holds
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.