Abstract
In this paper, we have developed a gene-gene relation browser (DiGG) that integrates sequential pattern-mining and information-extraction model to extract from biomedical literature knowledge on gene-gene interactions. DiGG combines efficient mining technique to enable the discovery of frequent gene-gene sequences even for very long sentences. Our approach aims to detect associated gene relations that are often discussed in documents. Integration of the related relations will lead to an individual gene relation network. Graphic presentation will be used to demonstrate the relationships between gene products. A salient feature of this approach is that it incrementally outputs new frequent gene relations in an online visualization fashion.
| Original language | English |
|---|---|
| Pages (from-to) | 1036-1041 |
| Number of pages | 6 |
| Journal | Expert Systems With Applications |
| Volume | 33 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2007 Nov 1 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
- Computer Science Applications
- Artificial Intelligence
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Discovering gene-gene relations from sequential sentence patterns in biomedical literature. / Chiang, Jung-Hsien; Liu, Hsiao-Sheng; Chao, Shih Yi; Chen, Cheng Yu.
In: Expert Systems With Applications, Vol. 33, No. 4, 01.11.2007, p. 1036-1041.Research output: Contribution to journal › Article
TY - JOUR
T1 - Discovering gene-gene relations from sequential sentence patterns in biomedical literature
AU - Chiang, Jung-Hsien
AU - Liu, Hsiao-Sheng
AU - Chao, Shih Yi
AU - Chen, Cheng Yu
PY - 2007/11/1
Y1 - 2007/11/1
N2 - In this paper, we have developed a gene-gene relation browser (DiGG) that integrates sequential pattern-mining and information-extraction model to extract from biomedical literature knowledge on gene-gene interactions. DiGG combines efficient mining technique to enable the discovery of frequent gene-gene sequences even for very long sentences. Our approach aims to detect associated gene relations that are often discussed in documents. Integration of the related relations will lead to an individual gene relation network. Graphic presentation will be used to demonstrate the relationships between gene products. A salient feature of this approach is that it incrementally outputs new frequent gene relations in an online visualization fashion.
AB - In this paper, we have developed a gene-gene relation browser (DiGG) that integrates sequential pattern-mining and information-extraction model to extract from biomedical literature knowledge on gene-gene interactions. DiGG combines efficient mining technique to enable the discovery of frequent gene-gene sequences even for very long sentences. Our approach aims to detect associated gene relations that are often discussed in documents. Integration of the related relations will lead to an individual gene relation network. Graphic presentation will be used to demonstrate the relationships between gene products. A salient feature of this approach is that it incrementally outputs new frequent gene relations in an online visualization fashion.
UR - http://www.scopus.com/inward/record.url?scp=33947678166&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33947678166&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2006.08.017
DO - 10.1016/j.eswa.2006.08.017
M3 - Article
VL - 33
SP - 1036
EP - 1041
JO - Expert Systems with Applications
JF - Expert Systems with Applications
SN - 0957-4174
IS - 4
ER -