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PLoS One. 2013 Dec 17;8(12):e84639. doi: 10.1371/journal.pone.0084639. eCollection 2013.

Discovering implicit entity relation with the gene-citation-gene network.

Author information

Department of Library and Information Science, Yonsei University, Seoul, Republic of Korea.
Department of Information and Library Science, Indiana University, Bloomington, Indiana, United States of America.

Erratum in

  • PLoS One. 2014;9(1). doi:10.1371/annotation/b6121731-7357-4657-9845-82eb0c937f89.


In this paper, we apply the entitymetrics model to our constructed Gene-Citation-Gene (GCG) network. Based on the premise there is a hidden, but plausible, relationship between an entity in one article and an entity in its citing article, we constructed a GCG network of gene pairs implicitly connected through citation. We compare the performance of this GCG network to a gene-gene (GG) network constructed over the same corpus but which uses gene pairs explicitly connected through traditional co-occurrence. Using 331,411 MEDLINE abstracts collected from 18,323 seed articles and their references, we identify 25 gene pairs. A comparison of these pairs with interactions found in BioGRID reveal that 96% of the gene pairs in the GCG network have known interactions. We measure network performance using degree, weighted degree, closeness, betweenness centrality and PageRank. Combining all measures, we find the GCG network has more gene pairs, but a lower matching rate than the GG network. However, combining top ranked genes in both networks produces a matching rate of 35.53%. By visualizing both the GG and GCG networks, we find that cancer is the most dominant disease associated with the genes in both networks. Overall, the study indicates that the GCG network can be useful for detecting gene interaction in an implicit manner.

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