Gene Expression

323 papers and 7.1k indexed citations i.

About

The 323 papers published in Gene Expression in the last decades have received a total of 7.1k indexed citations. Papers published in Gene Expression usually cover Molecular Biology (181 papers), Epidemiology (58 papers) and Hepatology (57 papers) specifically the topics of Liver Disease Diagnosis and Treatment (44 papers), Liver physiology and pathology (40 papers) and RNA Research and Splicing (23 papers). The most active scholars publishing in Gene Expression are John Y.L. Chiang, Jessica M. Ferrell, Satdarshan P. Monga, Hartmut Jaeschke, Takanori Konishi, Alex B. Lentsch, Paulette Bernd, Tsonwin Hai, Christopher C. Wolford and Anup Ramachandran.

In The Last Decade

Fields of papers published in Gene Expression

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers published in Gene Expression. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers published in Gene Expression.

Countries where authors publish in Gene Expression

Since Specialization
Citations

This map shows the geographic impact of research published in Gene Expression. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by papers published in Gene Expression with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gene Expression more than expected).

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar’s output or impact.

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