ArticleNucleic acids research2016
Identifying gene regulatory network rewiring using latent differential graphical models.
Article in Nucleic acids research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Variable expression ofProceedings of the National Academy of Sciences of the United States of America · 2024Pooled it
- Subgroup Analysis of Differential Networks with Latent Variables.Statistics and computing · 2025Article
- Double optimal transport for differential gene regulatory network inference with unpaired samples.Bioinformatics (Oxford, England) · 2025Article
- Patient-specific analysis of co-expression to measure biological network rewiring in individuals.Life science alliance · 2024Article
- Limitation of permutation-based differential correlation analysis.Genetic epidemiology · 2023Article
- A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.Bioinformatics advances · 2023Article
- An approach of gene regulatory network construction using mixed entropy optimizing context-related likelihood mutual information.Bioinformatics (Oxford, England) · 2023Article
- Inferring cancer common and specific gene networks via multi-layer joint graphical model.Computational and structural biotechnology journal · 2023Article
- Simultaneous differential network analysis and classification for matrix-variate data with application to brain connectivity.Biostatistics (Oxford, England) · 2022Article
- Identifying cancer pathway dysregulations using differential causal effects.Bioinformatics (Oxford, England) · 2022Article
- Identifying biomarkers for breast cancer by gene regulatory network rewiring.BMC bioinformatics · 2022Article
- Brain connectivity alteration detection via matrix-variate differential network model.Biometrics · 2021Article
- Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model.BMC genomics · 2021Article
- Sc-compReg enables the comparison of gene regulatory networks between conditions using single-cell data.Nature communications · 2021Article
- scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data.Genomics, proteomics & bioinformatics · 2021Article
- Scalable network estimation withStatistical analysis and data mining · 2021Article
- Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge.Frontiers in genetics · 2021Article
- Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression.International journal of molecular sciences · 2020Review
- Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data.BMC genomics · 2020Article
- DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring.BMC bioinformatics · 2020Article
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3 authors.
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Abstract
Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizing much smaller sample size to achieve reliable differential network estimation. Our simulation results demonstrated that LDGM consistently outperforms other Gaussian graphical model based methods. We further evaluated LDGM by applying to the brain and blood gene expression data from the GTEx consortium. We also applied LDGM to identify network rewiring between cancer subtypes using the TCGA breast cancer samples. Our results suggest that LDGM is an effective method to infer differential network using high-throughput gene expression data to identify GRN dynamics among different cellular conditions.
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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.