ArticlePLoS computational biology2016
FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- Algal origins of core land plant stress response subnetworks.The Plant journal : for cell and molecular biology · 2025Article
- A Selective Review of Network Analysis Methods for Gene Expression Data.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Markov field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques.Cell systems · 2024Article
- ONDSA: a testing framework based on Gaussian graphical models for differential and similarity analysis of multiple omics networks.Briefings in bioinformatics · 2024Article
- Markov Field network model of multi-modal data predicts effects of immune system perturbations on intravenous BCG vaccination in macaques.bioRxiv : the preprint server for biology · 2024Article
- Information-incorporated gene network construction with FDR control.Bioinformatics (Oxford, England) · 2024Article
- Anomaly detection in mixed high-dimensional molecular data.Bioinformatics (Oxford, England) · 2023Article
- Network-based approaches for modeling disease regulation and progression.Computational and structural biotechnology journal · 2023Review
- RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.PLoS computational biology · 2023Article
- Plasma p-tauAlzheimer's & dementia : the journal of the Alzheimer's Association · 2022Article
- Identifying cancer pathway dysregulations using differential causal effects.Bioinformatics (Oxford, England) · 2022Article
- Gap-com: general model selection criterion for sparse undirected gene networks with nontrivial community structure.G3 (Bethesda, Md.) · 2022Article
- Assisted estimation of gene expression graphical models.Genetic epidemiology · 2021Article
- Model guided trait-specific co-expression network estimation as a new perspective for identifying molecular interactions and pathways.PLoS computational biology · 2021Article
- Scalable network estimation withStatistical analysis and data mining · 2021Article
- Comparing Statistical Tests for Differential Network Analysis of Gene Modules.Frontiers in genetics · 2021Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Biological networks provide additional information for the analysis of human diseases, beyond the traditional analysis that focuses on single variables. Gaussian graphical model (GGM), a probability model that characterizes the conditional dependence structure of a set of random variables by a graph, has wide applications in the analysis of biological networks, such as inferring interaction or comparing differential networks. However, existing approaches are either not statistically rigorous or are inefficient for high-dimensional data that include tens of thousands of variables for making inference. In this study, we propose an efficient algorithm to implement the estimation of GGM and obtain p-value and confidence interval for each edge in the graph, based on a recent proposal by Ren et al., 2015. Through simulation studies, we demonstrate that the algorithm is faster by several orders of magnitude than the current implemented algorithm for Ren et al. without losing any accuracy. Then, we apply our algorithm to two real data sets: transcriptomic data from a study of childhood asthma and proteomic data from a study of Alzheimer's disease. We estimate the global gene or protein interaction networks for the disease and healthy samples. The resulting networks reveal interesting interactions and the differential networks between cases and controls show functional relevance to the diseases. In conclusion, we provide a computationally fast algorithm to implement a statistically sound procedure for constructing Gaussian graphical model and making inference with high-dimensional biological data. The algorithm has been implemented in an R package named "FastGGM".
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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.