Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/2040
Title: Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends
Authors: Jurca, G.
Addam, O.
Aksaç, A.
Gao, S.
Özyer, Tansel
Demetrick, D.
Alhajj, Reda
143116
Keywords: Data Mining 
 Extraction 
 manual curation
Issue Date: 2016
Publisher: BioMed Central Ltd.
Source: Jurca, G., Addam, O., Aksac, A., Gao, S., Özyer, T., Demetrick, D., & Alhajj, R. (2016). Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends. BMC research notes, 9(1), 236.
Abstract: Background: 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. Results: 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. Conclusions: 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. © 2016 Jurca et al.
URI: https://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-016-2023-5
https://hdl.handle.net/20.500.11851/2040
ISSN: 17560500
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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