Research status and collaboration analysis based on big data mining: an empirical study of Alzheimer's disease

ABSTRACT

This paper employs text mining techniques that aimed to facilitate technology information. First, this paper used patent data to monitor technological development trends systematically to show the technology research status from perspectives of country, institution, technology fields, and subjects. Secondly, this study explores the cooperation network institutions and inventors by applying the data mining approaches, social network analysis,. Additionally, the sequence analysis is applied to reveal a more comprehensive and objective appearance of cooperative relationships, partners, and centrality. The empirical findings reveal four significant observations. (1) The R&D centres have been mainly influenced by the United States and other developed countries. (2) All technological fields in both B IPC and Derwent manual codes are concentrated around pharmaceutical activities. (3) 1-6c alkyl, pharmaceutical composition, and central nervous system et al. are traditional research and core subjects. 2-6c alkenyl, amino acid sequence, and 1-3c alkoxy et al. are the hot subjects. (4) The influential institutions are HOFFMANN LA ROCHE & CO AG F (degree centrality is 0.0872), ASTRAZENECA AB, MERCK SHARP & DOHME CORP, PFIZER INC and UNIV CALIFORNIA, INCYTE GENOMICS. (5) The influential inventors are WANG Y, BACHER G, and PETERS D.

KEYWORDS:

• Research status
• data mining
• Alzheimer’s disease
• technology trend

Acknowledgments

This work was supported by the General Program of National Natural Science Foundation of China under (Grant Nos.71774012, 71673024) and the strategic research project of the Development Planning Bureau of the Chinese Academy of Sciences (Grant No. GHJ-ZLZX-2019-42). The findings and observations in this paper are those of the authors and do not necessarily reflect the views of the supporters.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Rongrong Li

Rongrong Li is a doctor from the School of Economics and Management Beijing Institute of Technology. In addition, she is also a teacher at School of Economics & Management, China University of Petroleum (East China). Her specialty is e-commerce, technology assessment and data mining. Her current research is focused on e-commerce, text mining and forecasting innovation pathways.

Xuefeng Wang

Xuefeng Wang is professor at the School of Management and Economics, Beijing Institute of Technology, China. His specialty is technology innovation management, data mining and science and technology evaluation. His current research emphasises measuring, mapping and forecasting innovation pathways.

Yuqin Liu

Yuqin Liu is professor at Beijing Green Printing and Packaging Industrial Technology Research Institute, Beijing Institue of Graphic Communication. His current research emphasises text mining, technology innovation assessment.

Shuo Zhang

Shuo Zhang is a doctor from the School of Economics and Management Beijing Institute of Technology. Her current research is data mining and forecasting innovation.

Omer Hanif

Omer Hanif is a doctor from the School of Economics and Management Beijing Institute of Technology. His specialty is knowledge management and text mining.