Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends
Identifieur interne : 000312 ( Main/Exploration ); précédent : 000311; suivant : 000313Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends
Auteurs : Gabriela Jurca [Canada] ; Omar Addam [Canada] ; Alper Aksac [Canada] ; Shang Gao [République populaire de Chine] ; Tansel Özyer [Turquie] ; Douglas Demetrick [Canada] ; Reda Alhajj [Canada, Liban]Source :
- BMC Research Notes [ 1756-0500 ] ; 2016.
Abstract
Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer.
We utilized PubMed for the testing. We investigated gene–gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries.
Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene–gene relations and gene functions.
The online version of this article (doi:10.1186/s13104-016-2023-5) contains supplementary material, which is available to authorized users.
Url:
DOI: 10.1186/s13104-016-2023-5
PubMed: 27112211
PubMed Central: 4845430
Affiliations:
- Canada, Liban, République populaire de Chine, Turquie
- Alberta, Dongbei, Jilin
- Calgary, Changchun
- Université de Calgary, Université de Jilin
Links toward previous steps (curation, corpus...)
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Le document en format XML
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Breast cancer is a serious disease which affects many women and may lead to death. It has received considerable attention from the research community. Thus, biomedical researchers aim to find genetic biomarkers indicative of the disease. Novel biomarkers can be elucidated from the existing literature. However, the vast amount of scientific publications on breast cancer make this a daunting task. This paper presents a framework which investigates existing literature data for informative discoveries. It integrates text mining and social network analysis in order to identify new potential biomarkers for breast cancer.</p>
</sec>
<sec><title>Results</title>
<p>We utilized PubMed for the testing. We investigated gene–gene interactions, as well as novel interactions such as gene-year, gene-country, and abstract-country to find out how the discoveries varied over time and how overlapping/diverse are the discoveries and the interest of various research groups in different countries.</p>
</sec>
<sec><title>Conclusions</title>
<p>Interesting trends have been identified and discussed, e.g., different genes are highlighted in relationship to different countries though the various genes were found to share functionality. Some text analysis based results have been validated against results from other tools that predict gene–gene relations and gene functions.</p>
</sec>
<sec><title>Electronic supplementary material</title>
<p>The online version of this article (doi:10.1186/s13104-016-2023-5) contains supplementary material, which is available to authorized users.</p>
</sec>
</div>
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<author><name sortKey="Liu, Et" uniqKey="Liu E">ET Liu</name>
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<author><name sortKey="Domanitskaya, Nv" uniqKey="Domanitskaya N">NV Domanitskaya</name>
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<li>Liban</li>
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<region><li>Alberta</li>
<li>Dongbei</li>
<li>Jilin</li>
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