Discovering implicit entity relation with the gene-citation-gene network.
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 connect...
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doaj-7bd9f2413ef440f8bdbb3c9cda8ff8872020-11-25T02:50:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01812e8463910.1371/journal.pone.0084639Discovering implicit entity relation with the gene-citation-gene network.Min SongNam-Gi HanYong-Hwan KimYing DingTamy ChambersIn 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.http://europepmc.org/articles/PMC3866152?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Min Song Nam-Gi Han Yong-Hwan Kim Ying Ding Tamy Chambers |
spellingShingle |
Min Song Nam-Gi Han Yong-Hwan Kim Ying Ding Tamy Chambers Discovering implicit entity relation with the gene-citation-gene network. PLoS ONE |
author_facet |
Min Song Nam-Gi Han Yong-Hwan Kim Ying Ding Tamy Chambers |
author_sort |
Min Song |
title |
Discovering implicit entity relation with the gene-citation-gene network. |
title_short |
Discovering implicit entity relation with the gene-citation-gene network. |
title_full |
Discovering implicit entity relation with the gene-citation-gene network. |
title_fullStr |
Discovering implicit entity relation with the gene-citation-gene network. |
title_full_unstemmed |
Discovering implicit entity relation with the gene-citation-gene network. |
title_sort |
discovering implicit entity relation with the gene-citation-gene network. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2013-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC3866152?pdf=render |
work_keys_str_mv |
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