ArticleBiometrics2022
Gaussian graphical model-based heterogeneity analysis via penalized fusion.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Robust Heterogeneity Adjustment for Gaussian Graphical Model With Latent Variables.Statistics in medicine · 2026Article
- MODEL-FREE INFERENCE FOR CHARACTERIZING PROTEIN MUTATIONS THROUGH A COEVOLUTIONARY LENS.The annals of applied statistics · 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
- The relationship between components of the biosafety incident response competence for clinical nursing staff: a network analysis.BMC psychiatry · 2025Article
- Subgroup Analysis of Differential Networks with Latent Variables.Statistics and computing · 2025Article
- Local Clustering for Functional Data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2025Article
- AJGM: joint learning of heterogeneous gene networks with adaptive graphical model.Bioinformatics (Oxford, England) · 2025Article
- A Selective Review of Network Analysis Methods for Gene Expression Data.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Heterogeneous latent transfer learning in Gaussian graphical models.Biometrics · 2024Article
- Estimation of multiple networks with common structures in heterogeneous subgroups.Journal of multivariate analysis · 2024Article
- Network-based cancer heterogeneity analysis incorporating multi-view of prior information.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Heterogeneity is a hallmark of cancer, diabetes, cardiovascular diseases, and many other complex diseases. This study has been partly motivated by the unsupervised heterogeneity analysis for complex diseases based on molecular and imaging data, for which, network-based analysis, by accommodating the interconnections among variables, can be more informative than that limited to mean, variance, and other simple distributional properties. In the literature, there has been very limited research on network-based heterogeneity analysis, and a common limitation shared by the existing techniques is that the number of subgroups needs to be specified a priori or in an ad hoc manner. In this article, we develop a penalized fusion approach for heterogeneity analysis based on the Gaussian graphical model. It applies penalization to the mean and precision matrix parameters to generate regularized and interpretable estimates. More importantly, a fusion penalty is imposed to "automatedly" determine the number of subgroups and generate more concise, reliable, and interpretable estimation. Consistency properties are rigorously established, and an effective computational algorithm is developed. The heterogeneity analysis of non-small-cell lung cancer based on single-cell gene expression data of the Wnt pathway and that of lung adenocarcinoma based on histopathological imaging data not only demonstrate the practical applicability of the proposed approach but also lead to interesting new findings.
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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.