ReviewFrontiers in genetics2021
Comparative Analyses of Gene Co-expression Networks: Implementations and Applications in the Study of Evolution.
Review in Frontiers in genetics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
- Problems With GO Enrichment as an Endpoint for Ecological Omics Interpretation.Molecular ecology · 2026Article
- Beyond the Classics: The Synergy of AI and Genomics Reveals an Expanded Repertoire of Pigmentation Genes.Journal of experimental zoology. Part B, Molecular and developmental evolution · 2026Review
- Machine Learning-Guided Stress Atlases Reveal Co-Expression Rewiring and Divergent Cellular Deployment of Abiotic Stress Programs in Rice and Wheat.International journal of molecular sciences · 2026Article
- CroCoNet: a framework for the quantitative comparison of gene regulatory networks across species.Genome biology · 2026Article
- Deep learning of 777 K bulk transcriptomes reveals human-mouse gene conservation beyond DNA sequence similarity.Communications biology · 2026Article
- Constructing gene co-functional and co-regulatory networks from public transcriptomes using condition-specific ensemble co-expression.Nature communications · 2026Article
- Analyzing Co-expression Networks with Network Skeleton Extraction.Research square · 2026Article
- Integrative multi-omics analysis reveals stress-specific molecular architectures in soybean under drought and rust infection.BMC genomics · 2026Article
- ANTIPODE Provides a Global View of Cell Type Homology and Transcriptomic Divergence in the Developing Mammalian Brain.bioRxiv : the preprint server for biology · 2025Article
- Developmental system drift and modular gene regulatory networks shape gastrulation inLife science alliance · 2025Article
- Dissecting lncRNA-mRNA regulatory network in type 2 diabetes as the risk factor of pancreatic cancer.Scientific reports · 2025Article
- Co-option of transcription factors drives evolution of quantitative disease resistance against a necrotrophic pathogen.The Plant cell · 2025Article
- Hybridization and gene expression: Beyond differentially expressed genes.Molecular ecology · 2025Review
- The Role of Pigments in Light Color Variation of the FireflyEcology and evolution · 2025Article
- A spectral framework to map QTLs affecting joint differential networks of gene co-expression.PLoS computational biology · 2025Article
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- Integrated gene co-expression network analysis and experimental validation identified integrin beta 1 as a candidate gene affecting milk production in dairy cattle.Journal of animal science · 2025Article
- CoRTE: a web-service for constructing temporal networks from genotype-tissue expression data.Bioinformatics advances · 2025Article
- Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.Cancer informatics · 2025Review
- Key LncRNAs Associated with Distant Metastasis in Breast Cancer: A System Biology Analysis.MicroRNA (Shariqah, United Arab Emirates) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Similarities and differences in the associations of biological entities among species can provide us with a better understanding of evolutionary relationships. Often the evolution of new phenotypes results from changes to interactions in pre-existing biological networks and comparing networks across species can identify evidence of conservation or adaptation. Gene co-expression networks (GCNs), constructed from high-throughput gene expression data, can be used to understand evolution and the rise of new phenotypes. The increasing abundance of gene expression data makes GCNs a valuable tool for the study of evolution in non-model organisms. In this paper, we cover motivations for why comparing these networks across species can be valuable for the study of evolution. We also review techniques for comparing GCNs in the context of evolution, including local and global methods of graph alignment. While some protein-protein interaction (PPI) bioinformatic methods can be used to compare co-expression networks, they often disregard highly relevant properties, including the existence of continuous and negative values for edge weights. Also, the lack of comparative datasets in non-model organisms has hindered the study of evolution using PPI networks. We also discuss limitations and challenges associated with cross-species comparison using GCNs, and provide suggestions for utilizing co-expression network alignments as an indispensable tool for evolutionary studies going forward.
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