ArticleProceedings. IEEE International Conference on Bioinformatics and Biomedicine2013
Network-based Pathway Enrichment Analysis.
Article in Proceedings. IEEE International Conference on Bioinformatics and Biomedicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Mini-Galaxy: Rethinking Complex Human Diseases Through the Lens of Systems Biology and Multilayered AI Network Perspectives.International journal of molecular sciences · 2026Review
- A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026Article
- Strategies to include prior knowledge in omics analysis with deep neural networks.Patterns (New York, N.Y.) · 2025Review
- Benchmarking enrichment analysis methods with the disease pathway network.Briefings in bioinformatics · 2024Article
- Molecular pathways enhance drug response prediction using transfer learning from cell lines to tumors and patient-derived xenografts.Scientific reports · 2022Article
- Role of adipocyte Na,K-ATPase oxidant amplification loop in cognitive decline and neurodegeneration.iScience · 2021Article
- Explainable drug sensitivity prediction through cancer pathway enrichment.Scientific reports · 2021Article
- Dissecting Pathway Disturbances Using Network Topology and Multi-platform Genomics Data.Statistics in biosciences · 2018Article
- Article
- EpCAM-Regulated Transcription Exerts Influences on Nanomechanical Properties of Endometrial Cancer Cells That Promote Epithelial-to-Mesenchymal Transition.Cancer research · 2016Article
- Gene set analysis: A step-by-step guide.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2015Review
- NET-GE: a novel NETwork-based Gene Enrichment for detecting biological processes associated to Mendelian diseases.BMC genomics · 2015Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
Finding out the associations between an input gene set, such as genes associated with a certain phenotype, and annotated gene sets, such as known pathways, are a very important problem in modern molecular biology. The existing approaches mainly focus on the overlap between the two, and may miss important but subtle relationships between genes. In this paper, we propose a method, NetPEA, by combining the known pathways and high-throughput networks. Our method not only considers the shared genes, but also takes the gene interactions into account. It utilizes a protein-protein interaction network and a random walk procedure to identify hidden relationships between gene sets, and uses a randomization strategy to evaluate the significance for pathways to achieve such similarity scores. Compared with the over-representation based method, our method can identify more relationships. Compared with a state of the art network-based method, EnrichNet, our method not only provides a ranked list of pathways, but also provides the statistical significant information. Importantly, through independent tests, we show that our method likely has a higher sensitivity in revealing the true casual pathways, while at the same time achieve a higher specificity. Literature review of selected results indicates that some of the novel pathways reported by our method are biologically relevant and important.
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Registered trials
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