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. 2021 Aug 3;11:15747. doi: 10.1038/s41598-021-94897-9

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© The Author(s) 2021, corrected publication 2023

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Workflow. Chart summarising the process from the downloading of the data to the detection and analysis of trends in the literature. (A) Creation of a graph database with the information contained in PubMed baseline 2020. (B) Acquisition of a comprehensive collection of human coding gene names and synonyms. (C) Automatic determination of potential ambiguous (unsafe) gene names. (D) Annotation of the graph database with unambiguous gene symbols by combining co-citation network topology and binary classifiers. (E) Prediction of per-gene publication trends using RNN. When a gene has significantly more publications or citations than expected by the model it is considered to be trendy. (F) Automatic topic detection of collections of publications. We used this algorithm to quantify the evolution of topics in trendy gene publications over time. (G) A review recommender system that uses information from the citation network and topic detection to recommend the most efficient set of reviews to explore the literature.