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

Md Facihul Azam; Aliyu Musa; Matthias Dehmer; Olli P Yli-Harja; Frank Emmert-Streib

doi:10.3389/fgene.2019.00070

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

Front Genet. 2019 Feb 14:10:70. doi: 10.3389/fgene.2019.00070. eCollection 2019.

Authors

Md Facihul Azam^{1

2}, Aliyu Musa^{1

2}, Matthias Dehmer^{3

4

5}, Olli P Yli-Harja^{2

6

7}, Frank Emmert-Streib^{1

2}

Affiliations

¹ Predictive Society and Data Analysis Lab, Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland.
² Institute of Biosciences and Medical Technology, Tampere, Finland.
³ Faculty for Management, Institute for Intelligent Production, University of Applied Sciences Upper Austria, Steyr, Austria.
⁴ Department of Mechatronics and Biomedical Computer Science, UMIT, Hall in Tyrol, Austria.
⁵ College of Computer and Control Engineering, Nankai University, Tianjin, China.
⁶ Computational Systems Biology, Faculty of Biomedical Engineering, Tampere University, Tampere, Finland.
⁷ Institute for Systems Biology, Seattle, WA, United States.

Abstract

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.

Keywords: biomedical text mining; computational network theory; genetics; meta-analysis; natural language processing; network science; prostate cancer; text mining.