Gene Set Knowledge Discovery with Enrichr
Impact in
- Immunology 304
Classified as
- Journal
- Current Protocols
In The Last Decade
doi.org/10.1002/cpz1.90 →Countries where authors are citing Gene Set Knowledge Discovery with Enrichr
This map shows the geographic impact of Gene Set Knowledge Discovery with Enrichr. 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 Gene Set Knowledge Discovery with Enrichr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gene Set Knowledge Discovery with Enrichr more than expected).
Fields of papers citing Gene Set Knowledge Discovery with Enrichr
This network shows the impact of Gene Set Knowledge Discovery with Enrichr. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Gene Set Knowledge Discovery with Enrichr.
About Gene Set Knowledge Discovery with Enrichr
This paper, published in 2021, received 2.2k indexed citations . Written by Zhuorui Xie, Allison M. Bailey, Maxim V. Kuleshov, Daniel J. B. Clarke, John Erol Evangelista, Sherry L. Jenkins, Alexander Lachmann, Megan L. Wojciechowicz, Eryk Kropiwnicki and Kathleen M. Jagodnik covering the research area of Molecular Biology. It is primarily cited by scholars working on Molecular Biology (995 citations), Immunology (304 citations), Cancer Research (268 citations), Oncology (207 citations) and Physiology (162 citations). Published in Current Protocols.
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.
This paper is also available at doi.org/10.1002/cpz1.90.