ArticleScientific reports2017
Discovering Condition-Specific Gene Co-Expression Patterns Using Gaussian Mixture Models: A Cancer Case Study.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Label-Free Clustering Analysis Platform Drives Cascaded Workflow for Scalable Production of Therapeutic Extracellular Vesicles.Journal of extracellular vesicles · 2026Article
- AJGM: joint learning of heterogeneous gene networks with adaptive graphical model.Bioinformatics (Oxford, England) · 2025Article
- Inhibition of CYP450 family 1 subfamily B member 1 (CYP1B1) expression in macrophage reduces the inflammatory response in type 2 diabetes mellitus combined with tuberculosis.Frontiers in endocrinology · 2025Article
- Article
- Vis-SPLIT: Interactive Hierarchical Modeling for mRNA Expression Classification.IEEE Visualization Conference : VIS. IEEE Conference on Visualization · 2023Article
- Evaluation and Exploration of Machine Learning and Convolutional Neural Network Classifiers in Detection of Lung Cancer from Microarray Gene-A Paradigm Shift.Bioengineering (Basel, Switzerland) · 2023Article
- Inferring cell diversity in single cell data using consortium-scale epigenetic data as a biological anchor for cell identity.Nucleic acids research · 2023Article
- RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.PLoS computational biology · 2023Article
- Identification of biomarkers associated with diagnosis of postmenopausal osteoporosis patients based on bioinformatics and machine learning.Frontiers in genetics · 2023Article
- Semiparametric modelling of two-component mixtures with stochastic dominance.Annals of the Institute of Statistical Mathematics · 2023Article
- EdgeCrafting: mining embedded, latent, nonlinear patterns to construct gene relationship networks.G3 (Bethesda, Md.) · 2022Article
- Addressing noise in co-expression network construction.Briefings in bioinformatics · 2022Review
- The Role of Network Science in Glioblastoma.Cancers · 2021Review
- The role of gene to gene interaction in the breast's genomic signature of pregnancy.Scientific reports · 2021Article
- Named Data Networking for Genomics Data Management and Integrated Workflows.Frontiers in big data · 2021Article
- An integrated approach to identify bimodal genes associated with prognosis in câncer.Genetics and molecular biology · 2021Article
- Screening of key biomarkers of tendinopathy based on bioinformatics and machine learning algorithms.PloS one · 2021Article
- Pan-cancer driver copy number alterations identified by joint expression/CNA data analysis.Scientific reports · 2020Article
- Exploration into biomarker potential of region-specific brain gene co-expression networks.Scientific reports · 2020Article
- NetExtractor: Extracting a Cerebellar Tissue Gene Regulatory Network Using Differentially Expressed High Mutual Information Binary RNA Profiles.G3 (Bethesda, Md.) · 2020Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A gene co-expression network (GCN) describes associations between genes and points to genetic coordination of biochemical pathways. However, genetic correlations in a GCN are only detectable if they are present in the sampled conditions. With the increasing quantity of gene expression samples available in public repositories, there is greater potential for discovery of genetic correlations from a variety of biologically interesting conditions. However, even if gene correlations are present, their discovery can be masked by noise. Noise is introduced from natural variation (intrinsic and extrinsic), systematic variation (caused by sample measurement protocols and instruments), and algorithmic and statistical variation created by selection of data processing tools. A variety of published studies, approaches and methods attempt to address each of these contributions of variation to reduce noise. Here we describe an approach using Gaussian Mixture Models (GMMs) to address natural extrinsic (condition-specific) variation during network construction from mixed input conditions. To demonstrate utility, we build and analyze a condition-annotated GCN from a compendium of 2,016 mixed gene expression data sets from five tumor subtypes obtained from The Cancer Genome Atlas. Our results show that GMMs help discover tumor subtype specific gene co-expression patterns (modules) that are significantly enriched for clinical attributes.
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