ArticleBMC bioinformatics2012
Network enrichment analysis: extension of gene-set enrichment analysis to gene networks.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.
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Who cites it
72 citing papers in PubMed.
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- Functional Interpretation of Omics Data: From Pathways to Networks.Advances in experimental medicine and biology · 2026Review
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- Combining VPS34 inhibitors with STING agonists enhances type I interferon signaling and anti-tumor efficacy.Molecular oncology · 2024Article
- Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles.Nucleic acids research · 2024Article
- Morphological connectivity differences in Alzheimer's disease correlate with gene transcription and cell-type.Human brain mapping · 2023Article
- Prediction model for drug response of acute myeloid leukemia patients.NPJ precision oncology · 2023Article
- AFB1 and OTA Promote Immune Toxicity in Human LymphoBlastic T Cells at Transcriptomic Level.Foods (Basel, Switzerland) · 2023Article
- Plasma RNA profiling unveils transcriptional signatures associated with resistance to osimertinib in EGFR T790M positive non-small cell lung cancer patients.Translational lung cancer research · 2022Article
- Discovery of druggable cancer-specific pathways with application in acute myeloid leukemia.GigaScience · 2022Article
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- PlantGSAD: a comprehensive gene set annotation database for plant species.Nucleic acids research · 2022Article
- Characterization and Validation of ceRNA-Mediated Pathway-Pathway Crosstalk Networks Across Eight Major Cardiovascular Diseases.Frontiers in cell and developmental biology · 2022Article
- Functional enrichment of alternative splicing events with NEASE reveals insights into tissue identity and diseases.Genome biology · 2021Article
- Integrative-omics for discovery of network-level disease biomarkers: a case study in Alzheimer's disease.Briefings in bioinformatics · 2021Article
- Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence.Cancers · 2021Review
- PIGNON: a protein-protein interaction-guided functional enrichment analysis for quantitative proteomics.BMC bioinformatics · 2021Article
- Targeted single-cell RNA sequencing of transcription factors enhances the identification of cell types and trajectories.Genome research · 2021Article
12 more citing papers are in PubMed but not listed here.
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8 authors.
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Abstract
backgroundGene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis.
resultsWe developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study.
conclusionsThe results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.
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