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

机译:基于全基因组的基于mesH的文献挖掘系统预测了隐含的基因 - 基因关系和网络

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Abstract 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.
机译:摘要背景后基因组学时代的大量文献使人们能够使用针对特定生物的所有可用文章来研究基因相互作用和网络。 MeSH是医学和科学术语的受控词汇表,生物医学科学家使用该词汇表手动索引PubMed文献数据库中的文章。我们假设来自PubMed文献数据库的全基因组基因-MeSH术语关联可用于预测隐式基因与基因的关系和网络。尽管在某些研究中,基因-MeSH关联已用于检测基因与基因的相互作用,但尚未很好地比较各种方法,并且尚未对全基因组文献分析评估这种策略。基因间相互作用的全基因组文献挖掘可对最佳基因相互作用进行排名,并在基因组水平上研究全面的生物网络。结果通过顺序生成基因-文章矩阵,归一化的基因-MeSH术语矩阵和基因-基因矩阵,开发了全基因组的GenoMesh文献挖掘算法。基因-基因矩阵依赖于基于基因-MeSH关系的成对基因差异计算。根据接收机工作特性(ROC)分析,从六个经过充分研究的函数中确定了一个优化的相异性得分。根据对大肠杆菌和研究较少的布鲁氏菌属的研究,发现GenoMesh可使用加权的MeSH术语准确地鉴定基因功能,预测文献中未报道的基因与基因的相互作用,以及将来自生物使用基于MeSH的基因-基因矩阵。也可以从以下网站获得基于Web的GenoMesh文献挖掘程序:http://genomesh.hegroup.org。 GenoMesh还可以预测与特定MeSH术语或用户选择的基因列表相关的基因之间的基因相互作用和网络。结论GenoMesh算法和Web程序提供了第一个基于MeSH的全基因组范围的文献挖掘系统,该系统可以有效地预测全基因组范围内的隐式基因-基因相互作用关系和网络。

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