Bibliometric analysis of machine learning in microbial ecology

Abstract:

[Background] With the rapid development of new types of stations and sequencing methods, the data in microbial ecology has experienced explosive growth, providing rich resources for revealing the role of microorganisms in global ecosystems. However, the processing and analysis of these large datasets pose challenges to conventional methods. Machine learning, with unique advantages in handling big data, has become a key technology to address these challenges. [Objective] This study comprehensively explored the developmental trends, current status, and hotspots in the application of machine learning in microbial ecology through bibliometric analysis, aiming to guide the future integration of machine learning with the research of microbial ecology. [Methods] The relevant articles published between 1991 and 2023 in the Web of Science (WOS) Core Collection were collected and analyzed. CiteSpace was used to visualize the evolution of the number of publications, international collaboration, and interdisciplinary status. Carrot2 was employed to mine textual data and build knowledge maps. [Results] The application of machine learning in the research of microbial ecology has undergone two distinct phases: stable growth followed by explosive growth, with 2018 marking a turning point. This research field has gained increasing attention, which led to continuous growth in the research output. The integration of machine learning with other disciplines, especially chemistry, physics, environmental sciences, and computer science, has become increasingly tight-knit, providing new perspectives for the advancement of scientific research. The application of machine learning in microbial ecology is extensive. The early studies primarily focused on sequence recognition and species identification. However, as deep learning and computer vision technologies have kept advancing since 2018, the research focus shifted towards predicting complex systems. The comparison of keywords between the two phases highlighted the evolution of machine learning in microbial ecology from basic data processing and analysis to complex and advanced prediction models. [Conclusion] According to the results of bibliometric analysis and considering challenges like data scarcity, difficulties in model selection, and poor interpretability in applying machine learning in microbial ecology, we suggest that international collaboration, data sharing, and interdisciplinary exchange should be emphasized in the future to promote the development of interpretable machine learning.