ArticleBioinformatics and biology insights2019
Cancer Genetic Network Inference Using Gaussian Graphical Models.
Article in Bioinformatics and biology insights, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Statistical genomics in rare cancer.Seminars in cancer biology · 2020Pooled it
- Identifying High-Dimensional Genomic Factors Modulating Biological Networks Across MultiOmics Data.bioRxiv : the preprint server for biology · 2025Article
- Psychometric Network Model Recovery: The Effect of Sample Size, Number of Items, and Number of Nodes.European journal of investigation in health, psychology and education · 2025Article
- sPGGM: a sample-perturbed Gaussian graphical model for identifying pre-disease stages and signaling molecules of disease progression.National science review · 2025Article
- Algal origins of core land plant stress response subnetworks.The Plant journal : for cell and molecular biology · 2025Article
- Uncovering critical transitions and molecule mechanisms in disease progressions using Gaussian graphical optimal transport.Communications biology · 2025Article
- Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods.BioData mining · 2023Article
- Anomaly detection in mixed high-dimensional molecular data.Bioinformatics (Oxford, England) · 2023Article
- Inferring cancer common and specific gene networks via multi-layer joint graphical model.Computational and structural biotechnology journal · 2023Article
- RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.PLoS computational biology · 2023Article
- Correlations between complex human phenotypes vary by genetic background, gender, and environment.Cell reports. Medicine · 2022Article
- Interaction-based transcriptome analysis via differential network inference.Briefings in bioinformatics · 2022Article
- Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks.Scientific reports · 2022Article
- Interpretation of network-based integration from multi-omics longitudinal data.Nucleic acids research · 2022Article
- Assisted estimation of gene expression graphical models.Genetic epidemiology · 2021Article
- scLink: Inferring Sparse Gene Co-expression Networks from Single-cell Expression Data.Genomics, proteomics & bioinformatics · 2021Article
- Network inference in systems biology: recent developments, challenges, and applications.Current opinion in biotechnology · 2020Review
- Joint learning of multiple gene networks from single-cell gene expression data.Computational and structural biotechnology journal · 2020Article
- HARMONIES: A Hybrid Approach for Microbiome Networks Inference via Exploiting Sparsity.Frontiers in genetics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Cancer Genome Atlas (TCGA) provides a rich resource that can be used to understand how genes interact in cancer cells and has collected RNA-Seq gene expression data for many types of human cancer. However, mining the data to uncover the hidden gene-interaction patterns remains a challenge. Gaussian graphical model (GGM) is often used to learn genetic networks because it defines an undirected graphical structure, revealing the conditional dependences of genes. In this study, we focus on inferring gene interactions in 15 specific types of human cancer using RNA-Seq expression data and GGM with graphical lasso. We take advantage of the corresponding Kyoto Encyclopedia of Genes and Genomes pathway maps to define the subsets of related genes. RNA-Seq expression levels of the subsets of genes in solid cancerous tumor and normal tissues were extracted from TCGA. The gene expression data sets were cleaned and formatted, and the genetic network corresponding to each cancer type was then inferred using GGM with graphical lasso. The inferred networks reveal stable conditional dependences among the genes at the expression level and confirm the essential roles played by the genes that encode proteins involved in the two key signaling pathway phosphoinositide 3-kinase (PI3K)/AKT/mTOR and Ras/Raf/MEK/ERK in human carcinogenesis. These stable dependences elucidate the expression level interactions among the genes that are implicated in many different human cancers. The inferred genetic networks were examined to further identify and characterize a collection of gene interactions that are unique to cancer. The cross-cancer genetic interactions revealed from our study provide another set of knowledge for cancer biologists to propose strong hypotheses, so further biological investigations can be conducted effectively.
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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 Socratic graph.