Analysis of the Trends in Biochemical Research Using Latent Dirichlet Allocation (LDA)

• Main Authors: Hee Jay Kang, Changhee Kim, Kyungtae Kang
• Format: Article
• Language: English
• Published: MDPI AG 2019-06-01
• Series: Processes
• Subjects: biochemistry; topic modeling; research trend; LDA
• Online Access: https://www.mdpi.com/2227-9717/7/6/379

Biochemistry has been broadly defined as “chemistry of molecules included or related to living systems”, but is becoming increasingly hard to be distinguished from other related fields. Targets of its studies evolve rapidly; some newly emerge, disappear, combine, or resurface themselves with a fresh viewpoint. Methodologies for biochemistry have been extremely diversified, thanks particularly to those adopted from molecular biology, synthetic chemistry, and biophysics. Therefore, this paper adopts topic modeling, a text mining technique, to identify the research topics in the field of biochemistry over the past twenty years and quantitatively analyze the changes in its trends. The results of the topic modeling analysis obtained through this study will provide a helpful tool for researchers, journal editors, publishers, and funding agencies to understand the connections among the diverse sub-fields in biochemical research and even see how the research topics branch out and integrate with other fields.