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A genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks

dc.contributor.authorXiang, Zuoshuang
dc.contributor.authorQin, Tingting
dc.contributor.authorQin, Zhaohui S
dc.contributor.authorHe, Yongqun
dc.date.accessioned2015-08-07T17:30:54Z
dc.date.available2015-08-07T17:30:54Z
dc.date.issued2013-10-16
dc.identifier.citationBMC Systems Biology. 2013 Oct 16;7(Suppl 3):S9
dc.identifier.urihttps://hdl.handle.net/2027.42/112478en_US
dc.description.abstractAbstract Background The large amount of literature in the post-genomics era enables the study of gene interactions and networks using all available articles published for a specific organism. MeSH is a controlled vocabulary of medical and scientific terms that is used by biomedical scientists to manually index articles in the PubMed literature database. We hypothesized that genome-wide gene-MeSH term associations from the PubMed literature database could be used to predict implicit gene-to-gene relationships and networks. While the gene-MeSH associations have been used to detect gene-gene interactions in some studies, different methods have not been well compared, and such a strategy has not been evaluated for a genome-wide literature analysis. Genome-wide literature mining of gene-to-gene interactions allows ranking of the best gene interactions and investigation of comprehensive biological networks at a genome level. Results The genome-wide GenoMesh literature mining algorithm was developed by sequentially generating a gene-article matrix, a normalized gene-MeSH term matrix, and a gene-gene matrix. The gene-gene matrix relies on the calculation of pairwise gene dissimilarities based on gene-MeSH relationships. An optimized dissimilarity score was identified from six well-studied functions based on a receiver operating characteristic (ROC) analysis. Based on the studies with well-studied Escherichia coli and less-studied Brucella spp., GenoMesh was found to accurately identify gene functions using weighted MeSH terms, predict gene-gene interactions not reported in the literature, and cluster all the genes studied from an organism using the MeSH-based gene-gene matrix. A web-based GenoMesh literature mining program is also available at: http://genomesh.hegroup.org. GenoMesh also predicts gene interactions and networks among genes associated with specific MeSH terms or user-selected gene lists. Conclusions The GenoMesh algorithm and web program provide the first genome-wide, MeSH-based literature mining system that effectively predicts implicit gene-gene interaction relationships and networks in a genome-wide scope.
dc.titleA genome-wide MeSH-based literature mining system predicts implicit gene-to-gene relationships and networks
dc.typeArticleen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/112478/1/12918_2013_Article_1166.pdf
dc.identifier.doi10.1186/1752-0509-7-S3-S9en_US
dc.language.rfc3066en
dc.rights.holderXiang et al.; licensee BioMed Central Ltd.
dc.date.updated2015-08-07T17:30:55Z
dc.owningcollnameInterdisciplinary and Peer-Reviewed


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