ArticleBioinformatics (Oxford, England)2008
Identifying gene-disease associations using centrality on a literature mined gene-interaction network.
Article in Bioinformatics (Oxford, England), 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers, 4 of them syntheses that pooled it.
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Who cites it
133 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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- Identifying potential survival strategies of HIV-1 through virus-host protein interaction networks.BMC systems biology · 2010Pooled it
- Literature-based discovery of IFN-gamma and vaccine-mediated gene interaction networks.Journal of biomedicine & biotechnology · 2010Pooled it
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- Denoising self-supervised learning for disease-gene association prediction.BMC bioinformatics · 2025Article
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- Decoding the therapeutic potential mechanism ofFrontiers in pharmacology · 2025Article
- Network proteomics of the Lewy body dementia brain reveals presynaptic signatures distinct from Alzheimer's disease.Molecular neurodegeneration · 2024Article
- Integrative analysis of metabolism subtypes and identification of prognostic metabolism-related genes for glioblastoma.Bioscience reports · 2024Article
- scDrugPrio: a framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases.Genome medicine · 2024Article
- Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values.BMC bioinformatics · 2024Article
- Network Proteomics of the Lewy Body Dementia Brain Reveals Presynaptic Signatures Distinct from Alzheimer's Disease.bioRxiv : the preprint server for biology · 2024Article
- A Scientometric Analysis and Visualization of Prosthetic Foot Research Work: 2000 to 2022.Bioengineering (Basel, Switzerland) · 2023Review
- GWAS Central: an expanding resource for finding and visualising genotype and phenotype data from genome-wide association studies.Nucleic acids research · 2023Article
- Identifying preeclampsia-associated genes using a control theory method.Briefings in functional genomics · 2022Article
73 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
motivationUnderstanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network.
resultsThe proposed approach can be used to extract known and to infer unknown gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more genes whose relation to the disease is currently unknown and are candidates for experimental study. AVAILABILITY: A web-based system for browsing the disease-specific gene-interaction networks is available at: http://gin.ncibi.org.
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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.