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Network Analysis of Cancer-focused Association Network Reveals Distinct Network Association Patterns.

Zhang Y, Tao C - Cancer Inform (2014)

Bottom Line: Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets.In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug-disease-gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts.Each NM corresponds to specific biological meanings.

View Article: PubMed Central - PubMed

Affiliation: Division of Biostatistics and Bioinformatics, University of Maryland Greenebaum Cancer Center, Baltimore, MD, USA. ; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, USA.

ABSTRACT
Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other biological entities (eg, genes and drugs) in cancer network, however, are usually scattered in biomedical publications. Systematic analyses of these cancer-specific associations can help highlight the hidden associations between different cancer types and related genes/drugs. In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug-disease-gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts. Eight significant NMs were identified and considered as the backbone of the cancer association network. Each NM corresponds to specific biological meanings. We demonstrated that such approaches will facilitate the formulization of novel cancer research hypotheses, which is critical for translational medicine research and personalized medicine in cancer.

No MeSH data available.


Related in: MedlinePlus

Degree distribution of three biomedical entities: cancer term, drug, and gene.
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f2-cin-suppl.3-2014-045: Degree distribution of three biomedical entities: cancer term, drug, and gene.

Mentions: Based on the NMs identified in the analysis, we constructed a core cancer–drug–gene network aggregated from significant NM instances. We then investigated the degree distribution of different types of entities in the integrated network. Figure 2 represents the degree distribution of cancer, drug, and gene nodes in the core cancer–disease–gene network. All three distributions follow the power-law distribution, indicating that networks related to different types of nodes are scale free. The majority of the nodes in the network have only a few (less than 10) links, but a few other nodes have a large number of links. Such distributions have been observed in many studies of biological networks.24 Our analysis demonstrates that in an integrated network consisting of heterogeneous associations, the scale-free network structure still holds. The hub nodes (ie, the nodes having a large number of links) can provide scientists future research directions.


Network Analysis of Cancer-focused Association Network Reveals Distinct Network Association Patterns.

Zhang Y, Tao C - Cancer Inform (2014)

Degree distribution of three biomedical entities: cancer term, drug, and gene.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4214591&req=5

f2-cin-suppl.3-2014-045: Degree distribution of three biomedical entities: cancer term, drug, and gene.
Mentions: Based on the NMs identified in the analysis, we constructed a core cancer–drug–gene network aggregated from significant NM instances. We then investigated the degree distribution of different types of entities in the integrated network. Figure 2 represents the degree distribution of cancer, drug, and gene nodes in the core cancer–disease–gene network. All three distributions follow the power-law distribution, indicating that networks related to different types of nodes are scale free. The majority of the nodes in the network have only a few (less than 10) links, but a few other nodes have a large number of links. Such distributions have been observed in many studies of biological networks.24 Our analysis demonstrates that in an integrated network consisting of heterogeneous associations, the scale-free network structure still holds. The hub nodes (ie, the nodes having a large number of links) can provide scientists future research directions.

Bottom Line: Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets.In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug-disease-gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts.Each NM corresponds to specific biological meanings.

View Article: PubMed Central - PubMed

Affiliation: Division of Biostatistics and Bioinformatics, University of Maryland Greenebaum Cancer Center, Baltimore, MD, USA. ; Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, USA.

ABSTRACT
Cancer is a complex and heterogeneous disease. Genetic methods have uncovered thousands of complex tissue-specific mutation-induced effects and identified multiple disease gene targets. Important associations between cancer and other biological entities (eg, genes and drugs) in cancer network, however, are usually scattered in biomedical publications. Systematic analyses of these cancer-specific associations can help highlight the hidden associations between different cancer types and related genes/drugs. In this paper, we proposed a novel network-based computational framework to identify statistically over-expressed subnetwork patterns called network motifs (NMs) in an integrated cancer-specific drug-disease-gene network extracted from Semantic MEDLINE, a database containing extracted associations from MEDLINE abstracts. Eight significant NMs were identified and considered as the backbone of the cancer association network. Each NM corresponds to specific biological meanings. We demonstrated that such approaches will facilitate the formulization of novel cancer research hypotheses, which is critical for translational medicine research and personalized medicine in cancer.

No MeSH data available.


Related in: MedlinePlus