ArticleBMC genomics2014
EgoNet: identification of human disease ego-network modules.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 32 citations in OpenAlex.
- Hypothesis generation for rare and undiagnosed diseases through clustering and classifying time-versioned biological ontologies.PloS one · 2024Article
- Genomic Analysis of Abnormal DNAM Methylation in Parathyroid Tumors.International journal of endocrinology · 2022Article
- DNLC: differential network local consistency analysis.BMC bioinformatics · 2019Article
- High throughput analysis to identify key gene molecules that inhibit adipogenic differentiation and promote osteogenic differentiation of human mesenchymal stem cells.Experimental and therapeutic medicine · 2019Article
- Systemic tracking of diagnostic function modules for post-menopausal osteoporosis in a differential co-expression network view.Experimental and therapeutic medicine · 2018Article
- Detection of Significant Pneumococcal Meningitis Biomarkers by Ego Network.Indian journal of pediatrics · 2017Article
- Network Marker Selection for Untargeted LC-MS Metabolomics Data.Journal of proteome research · 2017Article
- Investigating ego modules and pathways in osteosarcoma by integrating the EgoNet algorithm and pathway analysis.Brazilian journal of medical and biological research = Revista brasileira de pesquisas medicas e biologica · 2017Article
- Detecting subnetwork-level dynamic correlations.Bioinformatics (Oxford, England) · 2017Article
- Distilling a Visual Network of Retinitis Pigmentosa Gene-Protein Interactions to Uncover New Disease Candidates.PloS one · 2015Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundMining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks.
resultsWe present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes.
conclusionsOur results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases.
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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.