Limits...
Using unsupervised patterns to extract gene regulation relationships for network construction.

Tang YT, Li SJ, Kao HY, Tsai SJ, Wang HC - PLoS ONE (2011)

Bottom Line: The high scalability and low maintenance cost of the unsupervised patterns could help our system to extract gene expression from PubMed abstracts more precisely and effectively.Experiments on several regulators show reasonable precision and recall rates which validate AutoPat's practical applicability.The conducted regulation networks could also be built precisely and effectively.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Cheng Kung Tainan, Taiwan, Republic of China. p7895125@mail.ncku.edu.tw

ABSTRACT

Background: The gene expression is usually described in the literature as a transcription factor X that regulates the target gene Y. Previously, some studies discovered gene regulations by using information from the biomedical literature and most of them require effort of human annotators to build the training dataset. Moreover, the large amount of textual knowledge recorded in the biomedical literature grows very rapidly, and the creation of manual patterns from literatures becomes more difficult. There is an increasing need to automate the process of establishing patterns.

Methodology/principal findings: In this article, we describe an unsupervised pattern generation method called AutoPat. It is a gene expression mining system that can generate unsupervised patterns automatically from a given set of seed patterns. The high scalability and low maintenance cost of the unsupervised patterns could help our system to extract gene expression from PubMed abstracts more precisely and effectively.

Conclusions/significance: Experiments on several regulators show reasonable precision and recall rates which validate AutoPat's practical applicability. The conducted regulation networks could also be built precisely and effectively. The system in this study is available at http://ikmbio.csie.ncku.edu.tw/AutoPat/.

Show MeSH
The global network of HIF-1 TF.
© Copyright Policy
Related In: Results  -  Collection


getmorefigures.php?uid=PMC3091867&req=5

pone-0019633-g007: The global network of HIF-1 TF.

Mentions: In Figure 7, a four layers global gene regulatory network that contains direct and indirect relationships with HIF-1 TF and its partial network are shown. After the first TG set of HIF-1 is extracted, their confidence values are conducted by the occurrence frequency. If an extracted TG is also found in the TF dictionary, this TG is considered as the next TF for extracting generic regulatory relationships. The process is repeated four times. The extracted relationships are shown as a directed graph. The nodes represent TFs or TGs and the arcs are represented pairwise regulation relationships. An arc points from TF to TG and the type of relation is indicated as the generic regulatory relation. TGs in the layer one are denoted by green nodes. Furthermore, the blue, orange, and purple nodes indicate the other TGs from layer two to layer four. A high confidence regulation relation is denoted by a bold line. In this network, five well-studied regulation relationships are found by high confidence arcs. The VHL TG is regulated by HIF-1 TF directly in the layer one. The GC, RAR-alpha, MSK-1, and MOT1 TGs are indirectly related to HIF-1 TF in the layer four through some important nodes. VHL and TFIIB are found as important nodes between these indirectly related TGs with HIF-1 TF. The overall pathway information is listed in Table 7. Figure 8 shows the instances of direct and indirect generic regulatory relationships from HIF-1 TF to P53 TG. HIF-1 regulates P53 gene directly according to the articles (PMID: 11375890). Besides, P53 gene is also regulated by HIF-1 indirectly through P300 or VHL regulation processes.


Using unsupervised patterns to extract gene regulation relationships for network construction.

Tang YT, Li SJ, Kao HY, Tsai SJ, Wang HC - PLoS ONE (2011)

The global network of HIF-1 TF.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3091867&req=5

pone-0019633-g007: The global network of HIF-1 TF.
Mentions: In Figure 7, a four layers global gene regulatory network that contains direct and indirect relationships with HIF-1 TF and its partial network are shown. After the first TG set of HIF-1 is extracted, their confidence values are conducted by the occurrence frequency. If an extracted TG is also found in the TF dictionary, this TG is considered as the next TF for extracting generic regulatory relationships. The process is repeated four times. The extracted relationships are shown as a directed graph. The nodes represent TFs or TGs and the arcs are represented pairwise regulation relationships. An arc points from TF to TG and the type of relation is indicated as the generic regulatory relation. TGs in the layer one are denoted by green nodes. Furthermore, the blue, orange, and purple nodes indicate the other TGs from layer two to layer four. A high confidence regulation relation is denoted by a bold line. In this network, five well-studied regulation relationships are found by high confidence arcs. The VHL TG is regulated by HIF-1 TF directly in the layer one. The GC, RAR-alpha, MSK-1, and MOT1 TGs are indirectly related to HIF-1 TF in the layer four through some important nodes. VHL and TFIIB are found as important nodes between these indirectly related TGs with HIF-1 TF. The overall pathway information is listed in Table 7. Figure 8 shows the instances of direct and indirect generic regulatory relationships from HIF-1 TF to P53 TG. HIF-1 regulates P53 gene directly according to the articles (PMID: 11375890). Besides, P53 gene is also regulated by HIF-1 indirectly through P300 or VHL regulation processes.

Bottom Line: The high scalability and low maintenance cost of the unsupervised patterns could help our system to extract gene expression from PubMed abstracts more precisely and effectively.Experiments on several regulators show reasonable precision and recall rates which validate AutoPat's practical applicability.The conducted regulation networks could also be built precisely and effectively.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science and Information Engineering, National Cheng Kung Tainan, Taiwan, Republic of China. p7895125@mail.ncku.edu.tw

ABSTRACT

Background: The gene expression is usually described in the literature as a transcription factor X that regulates the target gene Y. Previously, some studies discovered gene regulations by using information from the biomedical literature and most of them require effort of human annotators to build the training dataset. Moreover, the large amount of textual knowledge recorded in the biomedical literature grows very rapidly, and the creation of manual patterns from literatures becomes more difficult. There is an increasing need to automate the process of establishing patterns.

Methodology/principal findings: In this article, we describe an unsupervised pattern generation method called AutoPat. It is a gene expression mining system that can generate unsupervised patterns automatically from a given set of seed patterns. The high scalability and low maintenance cost of the unsupervised patterns could help our system to extract gene expression from PubMed abstracts more precisely and effectively.

Conclusions/significance: Experiments on several regulators show reasonable precision and recall rates which validate AutoPat's practical applicability. The conducted regulation networks could also be built precisely and effectively. The system in this study is available at http://ikmbio.csie.ncku.edu.tw/AutoPat/.

Show MeSH