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Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer

View Article: PubMed Central - PubMed

ABSTRACT

Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.

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Related in: MedlinePlus

The procedures for extracting important KEGG pathways and GO terms of pancreatic cancer.
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fig1: The procedures for extracting important KEGG pathways and GO terms of pancreatic cancer.

Mentions: The purpose of this study is to extract important KEGG pathways and GO terms of pancreatic cancer using some computational methods. The detailed procedures are illustrated in Figure 1.


Analysis of Important Gene Ontology Terms and Biological Pathways Related to Pancreatic Cancer
The procedures for extracting important KEGG pathways and GO terms of pancreatic cancer.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5120232&req=5

fig1: The procedures for extracting important KEGG pathways and GO terms of pancreatic cancer.
Mentions: The purpose of this study is to extract important KEGG pathways and GO terms of pancreatic cancer using some computational methods. The detailed procedures are illustrated in Figure 1.

View Article: PubMed Central - PubMed

ABSTRACT

Pancreatic cancer is a serious disease that results in more than thirty thousand deaths around the world per year. To design effective treatments, many investigators have devoted themselves to the study of biological processes and mechanisms underlying this disease. However, it is far from complete. In this study, we tried to extract important gene ontology (GO) terms and KEGG pathways for pancreatic cancer by adopting some existing computational methods. Genes that have been validated to be related to pancreatic cancer and have not been validated were represented by features derived from GO terms and KEGG pathways using the enrichment theory. A popular feature selection method, minimum redundancy maximum relevance, was employed to analyze these features and extract important GO terms and KEGG pathways. An extensive analysis of the obtained GO terms and KEGG pathways was provided to confirm the correlations between them and pancreatic cancer.

No MeSH data available.


Related in: MedlinePlus