Research on Text Mining of Biomedical Field Based on Pubmed
Kang Li, Weidi Dai, Wenjun Wang, Ruixin Song
Available Online June 2016.
- https://doi.org/10.2991/mecs-17.2017.34How to use a DOI?
- Biomedical literature, research hotspot, co-word analysis, research collaboration, complex network analysis.
- The field of biomedical science is one of the most studied areas of the 21st century, the field has published a huge number of research papers, which have averaged more than 600,000 articles a year. How to effectively obtain knowledge in the vast literature of research is a challenge for researchers in the field. As one of the branches of bioinformatics, technology of text mining of biomedical field is a new exploration of the efficient and automated acquisition of relevant knowledge. In this paper, we take the example of genetic enhancement, articles related to Genetic Enhancement from 2005 to 2016 were selected as datasets. We focus on two approaches, co-word analysis is used to identify research hotspot and complex network analysis is used to analyze the collaboration network to determine the status of research collaboration in this field. From co-word analysis, we find that there are four research hotspots, they are gene expression related field, bioethical issues related field, metabolic and protein engineering related area and cell related field. From research collaboration analysis, we find that the research collaboration network has a scale-free feature and the max connected subgraph of it has a small world phenomenon. We also find that research collaboration in this field is now recovering.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Kang Li AU - Weidi Dai AU - Wenjun Wang AU - Ruixin Song PY - 2016/06 DA - 2016/06 TI - Research on Text Mining of Biomedical Field Based on Pubmed BT - Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017) PB - Atlantis Press SN - 2352-5401 UR - https://doi.org/10.2991/mecs-17.2017.34 DO - https://doi.org/10.2991/mecs-17.2017.34 ID - Li2016/06 ER -