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10.1186/s13104-016-2023-5 http://scihub22266oqcxt.onion/10.1186/s13104-016-2023-5 C4845430!4845430 !27112211     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27112211 &cmd=llinks): Failed to open stream: HTTP request failed! 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Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends |pdf=|usr=}}{{27112211 }} gab.com Text Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends JO: BMC Res Notes http://www.kidney.de/pget.php?&tw=1&pmid=27112211 #FOAvip 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. Twit Text FOAVip Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends ????BMC Res Notes http://www.kidney.de/pget.php?&tw=1&pmid=27112211 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27112211 #FOAvip English Wikipedia <ref>{{Cite pmid|27112211 }}</ref> Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends #MMPMID27112211 Jurca G ; Addam O ; Aksac A ; Gao S ; Özyer T ; Demetrick D ; Alhajj R BMC Res Notes 2016[Apr]; 9 (ä): 236 PMID27112211 show ga 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. |*Social Networking [MESH] |Biomarkers, Tumor/classification/*genetics [MESH] |Biomedical Research/statistics & numerical data [MESH] |Breast Neoplasms/diagnosis/*genetics [MESH] |Cluster Analysis [MESH] |Data Mining/*methods [MESH] |Epistasis, Genetic/genetics [MESH] |Female [MESH] |Gene Frequency [MESH] |Genetic Predisposition to Disease/genetics [MESH] |Geography [MESH] |Humans [MESH] |PubMed/statistics & numerical data [MESH]Integrating text mining, data mining, and network analysis for identif(epmc).pdf DeepDyve Pubget Overpricing