ArticleBiometrics2022
Information-incorporated Gaussian graphical model for gene expression data.
Article in Biometrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Joint modeling of mixed outcomes using a rank-based sparse neural network.Journal of biomedical informatics · 2025Article
- Single-cell analysis of gene regulatory networks in the mammary glands of P4HA1-knockout mice.PLoS genetics · 2025Article
- Bayesian Modeling of Cancer Outcomes Using Genetic Variables Assisted by Pathological Imaging Data.Statistics in medicine · 2025Article
- Estimation of multiple networks with common structures in heterogeneous subgroups.Journal of multivariate analysis · 2024Article
- Information-incorporated sparse hierarchical cancer heterogeneity analysis.Statistics in medicine · 2024Article
- Information-incorporated gene network construction with FDR control.Bioinformatics (Oxford, England) · 2024Article
- RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.PLoS computational biology · 2023Article
- Network-based cancer heterogeneity analysis incorporating multi-view of prior information.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
In the analysis of gene expression data, network approaches take a system perspective and have played an irreplaceably important role. Gaussian graphical models (GGMs) have been popular in the network analysis of gene expression data. They investigate the conditional dependence between genes and "transform" the problem of estimating network structures into a sparse estimation of precision matrices. When there is a moderate to large number of genes, the number of parameters to be estimated may overwhelm the limited sample size, leading to unreliable estimation and selection. In this article, we propose incorporating information from previous studies (for example, those deposited at PubMed) to assist estimating the network structure in the present data. It is recognized that such information can be partial, biased, or even wrong. A penalization-based estimation approach is developed, shown to have consistency properties, and realized using an effective computational algorithm. Simulation demonstrates its competitive performance under various information accuracy scenarios. The analysis of TCGA lung cancer prognostic genes leads to network structures different from the alternatives.
Indexed as
Identifiers
What Socratic 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 Socratic graph.