Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach

被引:6
作者
Azam, Md Facihul [1 ,2 ]
Musa, Aliyu [1 ,2 ]
Dehmer, Matthias [3 ,4 ,5 ]
Yli-Harja, Olli P. [2 ,6 ,7 ]
Emmert-Streib, Frank [1 ,2 ]
机构
[1] Tampere Univ, Predict Soc & Data Anal Lab, Fac Informat Technol & Commun Sci, Tampere, Finland
[2] Inst Biosci & Med Technol, Tampere, Finland
[3] Univ Appl Sci Upper Austria, Inst Intelligent Prod, Fac Management, Steyr, Austria
[4] UMIT, Dept Mechatron & Biomed Comp Sci, Hall In Tirol, Austria
[5] Nankai Univ, Coll Comp & Control Engn, Tianjin, Peoples R China
[6] Tampere Univ, Fac Biomed Engn, Computat Syst Biol, Tampere, Finland
[7] Inst Syst Biol, Seattle, WA USA
关键词
prostate cancer; text mining; natural language processing; network science; genetics; biomedical text mining; computational network theory; meta-analysis; ENRICHMENT; MEDICINE; ASSOCIATION;
D O I
10.3389/fgene.2019.00070
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
Prostate cancer is the most common cancer type in men in Finland and second worldwide. In this paper, we analyze almost 150, 000 published papers about prostate cancer, authored by ten thousands of scientists worldwide, with an integrated text mining and computational network theory approach. We demonstrate how to integrate text mining with network analysis investigating research contributions of countries and collaborations within and between countries. Furthermore, we study the time evolution of individually and collectively studied genes. Finally, we investigate a collaboration network of Finland and compare studied genes with globally studied genes in prostate cancer genetics. Overall, our results provide a global overview of prostate cancer research in genetics. In addition, we present a specific discussion for Finland. Our results shed light on trends within the last 30 years and are useful for translational researchers within the full range from genetics to public health management and health policy.
引用
收藏
页数:18
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