ArticleScientific reports2015
Functional Analysis and Characterization of Differential Coexpression Networks.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 52 citations in OpenAlex.
- Updating genome annotation for the microbial cell factory Aspergillus niger using gene co-expression networks.Nucleic acids research · 2019Pooled it
- MIAAIM: Multi-omics image integration with dimensional reduction for tissue state mapping.PLoS computational biology · 2026Article
- Analyzing Co-expression Networks with Network Skeleton Extraction.Research square · 2026Article
- Differential network analysis of oral microbiome metatranscriptomes identifies community scale metabolic restructuring in dental caries.PNAS nexus · 2022Article
- Dynamic rewiring of biological activity across genotype and lineage revealed by context-dependent functional interactions.Genome biology · 2022Article
- Re-wiring and gene expression changes of AC025034.1 and ATP2B1 play complex roles in early-to-late breast cancer progression.BMC genomic data · 2022Article
- Potential biomarkers and molecular mechanisms in preeclampsia progression.Open life sciences · 2022Article
- Data analysis methods for defining biomarkers from omics data.Analytical and bioanalytical chemistry · 2022Review
- Identifying the degree of genetic interactions using Restricted Boltzmann Machine-A study on colorectal cancer.IET systems biology · 2021Article
- Systemic lipid dysregulation is a risk factor for macular neurodegenerative disease.Scientific reports · 2020Article
- Unraveling the role of salt-sensitivity genes in obesity with integrated network biology and co-expression analysis.PloS one · 2020Article
- Sex biased expression and co-expression networks in development, using the hymenopteran Nasonia vitripennis.PLoS genetics · 2020Article
- Article
- Identification of hyper-rewired genomic stress non-oncogene addiction genes across 15 cancer types.NPJ systems biology and applications · 2019Article
- Article
- Functional exploration of co-expression networks identifies a nexus for modulating protein and citric acid titres inFungal biology and biotechnology · 2019Article
- Large-scale profiling of noncoding RNA function in yeast.PLoS genetics · 2018Article
- MultiDCoX: Multi-factor analysis of differential co-expression.BMC bioinformatics · 2017Article
- Gene expression analysis in asthma using a targeted multiplex array.BMC pulmonary medicine · 2017Article
- Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer.Scientific reports · 2017Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differential coexpression analysis is emerging as a complement to conventional differential gene expression analysis. The identified differential coexpression links can be assembled into a differential coexpression network (DCEN) in response to environmental stresses or genetic changes. Differential coexpression analyses have been successfully used to identify condition-specific modules; however, the structural properties and biological significance of general DCENs have not been well investigated. Here, we analyzed two independent Saccharomyces cerevisiae DCENs constructed from large-scale time-course gene expression profiles in response to different situations. Topological analyses show that DCENs are tree-like networks possessing scale-free characteristics, but not small-world. Functional analyses indicate that differentially coexpressed gene pairs in DCEN tend to link different biological processes, achieving complementary or synergistic effects. Furthermore, the gene pairs lacking common transcription factors are sensitive to perturbation and hence lead to differential coexpression. Based on these observations, we integrated transcriptional regulatory information into DCEN and identified transcription factors that might cause differential coexpression by gain or loss of activation in response to different situations. Collectively, our results not only uncover the unique structural characteristics of DCEN but also provide new insights into interpretation of DCEN to reveal its biological significance and infer the underlying gene regulatory dynamics.
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