An exploratory text analysis of the autophagy research field

• Published in: Autophagy Vol. 18; no. 7; pp. 1648 - 1661
• Main Authors: Yim, Willa Wen-You, Kurikawa, Yoshitaka, Mizushima, Noboru
• Format: Journal Article
• Language: English
• Published: United States Taylor & Francis 03.07.2022
• Subjects: Autophagy; Autophagy - genetics; Data Mining - methods; Research Paper; RNA, Untranslated; text analysis; text analytics; text mining; topic modeling; text analysis; topic modeling; text mining; Autophagy; text analytics
• DOI: 10.1080/15548627.2021.1995151
• ISSN: 1554-8627; 1554-8635

After its discovery in the 1950 s, the autophagy research field has seen its annual number of publications climb from tens to thousands. The ever-growing number of autophagy publications is a wealth of information but presents a challenge to researchers, especially those new to the field, who are looking for a general overview of the field to, for example, determine current topics of the field or formulate new hypotheses. Here, we employed text mining tools to extract research trends in the autophagy field, including those of genes, terms, and topics. The publication trend of the field can be separated into three phases. The exponential rise in publication number began in the last phase and is most likely spurred by a series of highly cited research papers published in previous phases. The exponential increase in papers has resulted in a larger variety of research topics, with the majority involving those that are directly physiologically relevant, such as disease and modulating autophagy. Our findings provide researchers a summary of the history of the autophagy research field and perhaps hints of what is to come. Abbreviations: 5Y-IF: 5-year impact factor; AIS: article influence score; EM: electron microscopy; HGNC: HUGO gene nomenclature committee; LDA: latent Dirichlet allocation; MeSH: medical subject headings; ncRNA: non-coding RNA