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

Min Song, Nam Gi Han, Yong Hwan Kim, Ying Ding, Tamy Chambers

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article numbere84639
JournalPLoS One
Volume8
Issue number12
DOIs
Publication statusPublished - 2013 Dec 17

Fingerprint

Gene Regulatory Networks
Genes
genes
gene regulatory networks
gene interaction

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Song, Min ; Han, Nam Gi ; Kim, Yong Hwan ; Ding, Ying ; Chambers, Tamy. / Discovering implicit entity relation with the gene-citation-gene network. In: PLoS One. 2013 ; Vol. 8, No. 12.
@article{df5945622b1148fb94f6cb827bce58b2,
title = "Discovering implicit entity relation with the gene-citation-gene network",
abstract = "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.",
author = "Min Song and Han, {Nam Gi} and Kim, {Yong Hwan} and Ying Ding and Tamy Chambers",
year = "2013",
month = "12",
day = "17",
doi = "10.1371/journal.pone.0084639",
language = "English",
volume = "8",
journal = "PLoS One",
issn = "1932-6203",
publisher = "Public Library of Science",
number = "12",

}

Discovering implicit entity relation with the gene-citation-gene network. / Song, Min; Han, Nam Gi; Kim, Yong Hwan; Ding, Ying; Chambers, Tamy.

In: PLoS One, Vol. 8, No. 12, e84639, 17.12.2013.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Discovering implicit entity relation with the gene-citation-gene network

AU - Song, Min

AU - Han, Nam Gi

AU - Kim, Yong Hwan

AU - Ding, Ying

AU - Chambers, Tamy

PY - 2013/12/17

Y1 - 2013/12/17

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84892918492&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84892918492&partnerID=8YFLogxK

U2 - 10.1371/journal.pone.0084639

DO - 10.1371/journal.pone.0084639

M3 - Article

VL - 8

JO - PLoS One

JF - PLoS One

SN - 1932-6203

IS - 12

M1 - e84639

ER -