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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

Overview of the network-based computational framework for an integrated cancer–drug–disease network.
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f1-cin-suppl.3-2014-045: Overview of the network-based computational framework for an integrated cancer–drug–disease network.

Mentions: To comprehensively investigate the integrated cancer–drug–gene network formed by associations available in Semantic MEDLINE, we proposed the following two-step computational framework: (1) extraction and optimization of cancer–drug–gene network in Semantic MEDLINE and (2) network topology analysis of this heterogeneous network at two levels: statistics and degree distribution of high-confidence association networks, and distinct pattern detection at the NM level. In this section, we first describe the steps to extract association network data from MEDLINE database, followed by a description of the proposed network-based approach to investigate this heterogeneous drug–disease–gene association network. Figure 1 illustrates the steps of the proposed approach.


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

Zhang Y, Tao C - Cancer Inform (2014)

Overview of the network-based computational framework for an integrated cancer–drug–disease network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-cin-suppl.3-2014-045: Overview of the network-based computational framework for an integrated cancer–drug–disease network.
Mentions: To comprehensively investigate the integrated cancer–drug–gene network formed by associations available in Semantic MEDLINE, we proposed the following two-step computational framework: (1) extraction and optimization of cancer–drug–gene network in Semantic MEDLINE and (2) network topology analysis of this heterogeneous network at two levels: statistics and degree distribution of high-confidence association networks, and distinct pattern detection at the NM level. In this section, we first describe the steps to extract association network data from MEDLINE database, followed by a description of the proposed network-based approach to investigate this heterogeneous drug–disease–gene association network. Figure 1 illustrates the steps of the proposed approach.

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