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Clinic-genomic association mining for colorectal cancer using publicly available datasets.

Liu F, Feng Y, Li Z, Pan C, Su Y, Yang R, Song L, Duan H, Deng N - Biomed Res Int (2014)

Bottom Line: However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease.A total of 23,517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones.Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China.

ABSTRACT
In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis. We applied this method to datasets extracted from both gene expression omnibus (GEO) and genetic association database (GAD). A total of 23,517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones. Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored. The proposed method is effective in mining valuable knowledge from available biomedical big data and achieves a good performance in bridging clinical data with genomic data for colorectal cancer.

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Related in: MedlinePlus

Quantitative evaluation of association degree between genes from GEO with colorectal cancer. The horizontal axis presents the number of association related to GSE, while the vertical axis presents the relevance score computed using formula (1).
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fig3: Quantitative evaluation of association degree between genes from GEO with colorectal cancer. The horizontal axis presents the number of association related to GSE, while the vertical axis presents the relevance score computed using formula (1).

Mentions: Figure 3 demonstrates the relationship between “Relevance score” and the number of relation related GSE. It can be seen that the “Relevance score” increases with the increases of related GSE numbers. This trend can be interpreted from the following perspective. Related GSE are data foundation of clinic-genomic associations. Therefore, more related GSE indicates much more reliable clinic-genomic associations about colorectal cancer and thus the closer association between genes and colorectal cancer.


Clinic-genomic association mining for colorectal cancer using publicly available datasets.

Liu F, Feng Y, Li Z, Pan C, Su Y, Yang R, Song L, Duan H, Deng N - Biomed Res Int (2014)

Quantitative evaluation of association degree between genes from GEO with colorectal cancer. The horizontal axis presents the number of association related to GSE, while the vertical axis presents the relevance score computed using formula (1).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4060771&req=5

fig3: Quantitative evaluation of association degree between genes from GEO with colorectal cancer. The horizontal axis presents the number of association related to GSE, while the vertical axis presents the relevance score computed using formula (1).
Mentions: Figure 3 demonstrates the relationship between “Relevance score” and the number of relation related GSE. It can be seen that the “Relevance score” increases with the increases of related GSE numbers. This trend can be interpreted from the following perspective. Related GSE are data foundation of clinic-genomic associations. Therefore, more related GSE indicates much more reliable clinic-genomic associations about colorectal cancer and thus the closer association between genes and colorectal cancer.

Bottom Line: However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease.A total of 23,517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones.Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China.

ABSTRACT
In recent years, a growing number of researchers began to focus on how to establish associations between clinical and genomic data. However, up to now, there is lack of research mining clinic-genomic associations by comprehensively analysing available gene expression data for a single disease. Colorectal cancer is one of the malignant tumours. A number of genetic syndromes have been proven to be associated with colorectal cancer. This paper presents our research on mining clinic-genomic associations for colorectal cancer under biomedical big data environment. The proposed method is engineered with multiple technologies, including extracting clinical concepts using the unified medical language system (UMLS), extracting genes through the literature mining, and mining clinic-genomic associations through statistical analysis. We applied this method to datasets extracted from both gene expression omnibus (GEO) and genetic association database (GAD). A total of 23,517 clinic-genomic associations between 139 clinical concepts and 7914 genes were obtained, of which 3474 associations between 31 clinical concepts and 1689 genes were identified as highly reliable ones. Evaluation and interpretation were performed using UMLS, KEGG, and Gephi, and potential new discoveries were explored. The proposed method is effective in mining valuable knowledge from available biomedical big data and achieves a good performance in bridging clinical data with genomic data for colorectal cancer.

Show MeSH
Related in: MedlinePlus