Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers

Dongyu Xu; Bing Liu; Jian Wang; Zhichang Zhang

doi:10.3389/fbioe.2022.998298

Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers

Front Bioeng Biotechnol. 2022 Oct 7:10:998298. doi: 10.3389/fbioe.2022.998298. eCollection 2022.

Authors

Dongyu Xu¹, Bing Liu², Jian Wang³, Zhichang Zhang¹

Affiliations

¹ Department of Computer, School of Intelligent Medicine, China Medical University, Shenyang, Liaoning, China.
² Department of Bone Oncology, The People's Hospital of Liaoning Province, Shenyang, Liaoning, China.
³ Department of Pathogenic Biology, School of Basic Medicine, China Medical University, Shenyang, Liaoning, China.

Abstract

Background: In the biotechnology and applied microbiology sectors, artificial intelligence (AI) has been extensively used in disease diagnostics, drug research and development, functional genomics, biomarker recognition, and medical imaging diagnostics. In our study, from 2000 to 2021, science publications focusing on AI in biotechnology were reviewed, and quantitative, qualitative, and modeling analyses were performed. Methods: On 6 May 2022, the Web of Science Core Collection (WoSCC) was screened for AI applications in biotechnology and applied microbiology; 3,529 studies were identified between 2000 and 2022, and analyzed. The following information was collected: publication, country or region, references, knowledgebase, institution, keywords, journal name, and research hotspots, and examined using VOSviewer and CiteSpace V bibliometric platforms. Results: We showed that 128 countries published articles related to AI in biotechnology and applied microbiology; the United States had the most publications. In addition, 584 global institutions contributed to publications, with the Chinese Academy of Science publishing the most. Reference clusters from studies were categorized into ten headings: deep learning, prediction, support vector machines (SVM), object detection, feature representation, synthetic biology, amyloid, human microRNA precursors, systems biology, and single cell RNA-Sequencing. Research frontier keywords were represented by microRNA (2012-2020) and protein-protein interactions (PPIs) (2012-2020). Conclusion: We systematically, objectively, and comprehensively analyzed AI-related biotechnology and applied microbiology literature, and additionally, identified current hot spots and future trends in this area. Our review provides researchers with a comprehensive overview of the dynamic evolution of AI in biotechnology and applied microbiology and identifies future key research areas.

Keywords: applied microbiology; artificial intelligence; bibliometric; biotechnology; deep learning; machine learning.

Publication types

Review