Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000–2021] - Zhang, Bijun; Fan, Ting (Frontiers in Genetics, 2022)
Introduction: Deep Learning technology has been widely used in genetic research because of its characteristics of Computability, Statistical analysis and predictability. Herein, we aimed to summarize standardized knowledge and potential innovative approaches for Deep Learning applications of genetics by evaluating publications published from different countries and to encourage more researches on efficient strategies. Methods: The Science Citation Index Expanded TM (SCIE) database was searched on Dec 22, 2021 for deep learning applications for genomics related publications. Original articles and reviews were considered. In this study, we derived a clustered network from 69,806 references that were cited by the 1,754 related manuscripts identified. We used CiteSpace, and VOSviewer to identify countries, institutions, journals, co-cited references, keywords, subject evolution, path, current characteristics and future trajectories. Results: We assessed the rapid increasing publications concerned about Deep Learning applications of genomics approaches and identified 1,754 papers that published reports focusing on this subject. Among these, a total of 101 countries and 2,487 institutes contributed publications, The United States of America had the most publications (728/1754) and the highest h-index, US have been in close collaborations with China and Germany. The reference clusters of SCI papers were clustered into seven categories: deep learning, logic regression, variant prioritization, random forests, scRNA-seq, genomic regulation, recombination. The keywords representing the research frontiers by year were prediction (2016-2021), sequence (2017-2021), mutation (2017-2021), cancer (2019-2021). Conclusions: Here we summarized the current literature related to the status of Deep Learning for genetics applications and analyzed the current research characteristics and the future trajectories in this field. This work aims to provide resources for possible further intensive exploration and encourage more researchers to overcome the research of Deep Learning applications in genetics.