ArticleNucleic acids research2005
Integration of text- and data-mining using ontologies successfully selects disease gene candidates.
Article in Nucleic acids research, 2005. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
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Who cites it
63 citing papers in PubMed, 210 citations in OpenAlex.
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- Literature-Based Enrichment Insights into Redox Control of Vascular Biology.Oxidative medicine and cellular longevity · 2019Review
- An integrative network-based approach to identify novel disease genes and pathways: a case study in the context of inflammatory bowel disease.BMC bioinformatics · 2018Article
- Mimvec: a deep learning approach for analyzing the human phenome.BMC systems biology · 2017Article
- DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species.Scientific reports · 2017Article
- Recent Advances and Emerging Applications in Text and Data Mining for Biomedical Discovery.Briefings in bioinformatics · 2016Review
- Gene-Disease Interaction Retrieval from Multiple Sources: A Network Based Method.BioMed research international · 2016Article
- MOBAS: identification of disease-associated protein subnetworks using modularity-based scoring.EURASIP journal on bioinformatics & systems biology · 2015Article
- Systematic Analysis of Integrated Gene Functional Network of Four Chronic Stress-related Lifestyle Disorders.Genome integrity · 2015Article
- Pinpointing disease genes through phenomic and genomic data fusion.BMC genomics · 2015Article
- Identification of highly related references about gene-disease association.BMC bioinformatics · 2014Article
- Article
- Text mining in cancer gene and pathway prioritization.Cancer informatics · 2014Review
- Associating disease-related genetic variants in intergenic regions to the genes they impact.PeerJ · 2014Article
- Approaches for recognizing disease genes based on network.BioMed research international · 2014Article
- Integrating human omics data to prioritize candidate genes.BMC medical genomics · 2013Article
- Gene prioritization of resistant rice gene against Xanthomas oryzae pv. oryzae by using text mining technologies.BioMed research international · 2013Article
- Mining the pharmacogenomics literature--a survey of the state of the art.Briefings in bioinformatics · 2012Article
- Biomarker identification for prostate cancer and lymph node metastasis from microarray data and protein interaction network using gene prioritization method.TheScientificWorldJournal · 2012Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Genome-wide techniques such as microarray analysis, Serial Analysis of Gene Expression (SAGE), Massively Parallel Signature Sequencing (MPSS), linkage analysis and association studies are used extensively in the search for genes that cause diseases, and often identify many hundreds of candidate disease genes. Selection of the most probable of these candidate disease genes for further empirical analysis is a significant challenge. Additionally, identifying the genes that cause complex diseases is problematic due to low penetrance of multiple contributing genes. Here, we describe a novel bioinformatic approach that selects candidate disease genes according to their expression profiles. We use the eVOC anatomical ontology to integrate text-mining of biomedical literature and data-mining of available human gene expression data. To demonstrate that our method is successful and widely applicable, we apply it to a database of 417 candidate genes containing 17 known disease genes. We successfully select the known disease gene for 15 out of 17 diseases and reduce the candidate gene set to 63.3% (+/-18.8%) of its original size. This approach facilitates direct association between genomic data describing gene expression and information from biomedical texts describing disease phenotype, and successfully prioritizes candidate genes according to their expression in disease-affected tissues.
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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.