ArticleGenes2017
Pathway Enrichment Analysis with Networks.
Article in Genes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- A Cross-Generational Methylomic Signature of Infant Maltreatment in Newborn Rhesus Macaques.Biological psychiatry · 2025Article
- ToxAssay: a hierarchical model-driven tool for advanced toxicogenomics biomarker discovery.Bioinformatics (Oxford, England) · 2025Article
- Cluefish: mining the dark matter of transcriptional data series with over-representation analysis enhanced by aggregated biological prior knowledge.NAR genomics and bioinformatics · 2025Article
- Network based systems biology approach to identify diseasome and comorbidity associations of Systemic Sclerosis with cancers.Heliyon · 2022Article
- Transcriptome analysis revealed cadmium accumulation mechanisms in hyperaccumulator Siegesbeckia orientalis L.Environmental science and pollution research international · 2020Article
- Understanding of Zaire ebolavirus-human protein interaction for drug repurposing.Virusdisease · 2020Article
- Investigating the deregulation of metabolic tasks via Minimum Network Enrichment Analysis (MiNEA) as applied to nonalcoholic fatty liver disease using mouse and human omics data.PLoS computational biology · 2019Article
- pathfindR: An R Package for Comprehensive Identification of Enriched Pathways in Omics Data Through Active Subnetworks.Frontiers in genetics · 2019Article
- Integrative Differential Expression Analysis for Multiple EXperiments (IDEAMEX): A Web Server Tool for Integrated RNA-Seq Data Analysis.Frontiers in genetics · 2019Article
- Interferon-stimulated genes: new platforms and computational approaches.Mammalian genome : official journal of the International Mammalian Genome Society · 2018Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Detecting associations between an input gene set and annotated gene sets (e.g., pathways) is an important problem in modern molecular biology. In this paper, we propose two algorithms, termed NetPEA and NetPEA', for conducting network-based pathway enrichment analysis. Our algorithms consider not only shared genes but also gene-gene interactions. Both algorithms utilize a protein-protein interaction network and a random walk with a restart procedure to identify hidden relationships between an input gene set and pathways, but both use different randomization strategies to evaluate statistical significance and as a result emphasize different pathway properties. Compared to an over representation-based method, our algorithms can identify more statistically significant pathways. Compared to an existing network-based algorithm, EnrichNet, our algorithms have a higher sensitivity in revealing the true causal pathways while at the same time achieving a higher specificity. A literature review of selected results indicates that some of the novel pathways reported by our algorithms are biologically relevant and important. While the evaluations are performed only with KEGG pathways, we believe the algorithms can be valuable for general functional discovery from high-throughput experiments.
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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 OpenQuestion graph.