An integrated model of the recognition of Candida albicans by the innate immune system
- Journal
- Nature Reviews Microbiology
In The Last Decade
doi.org/10.1038/nrmicro1815 →Countries where authors are citing An integrated model of the recognition of Candida albicans by the innate immune system
This map shows the geographic impact of An integrated model of the recognition of Candida albicans by the innate immune system. 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 An integrated model of the recognition of Candida albicans by the innate immune system with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites An integrated model of the recognition of Candida albicans by the innate immune system more than expected).
Fields of papers citing An integrated model of the recognition of Candida albicans by the innate immune system
This network shows the impact of An integrated model of the recognition of Candida albicans by the innate immune system. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the An integrated model of the recognition of Candida albicans by the innate immune system.
About An integrated model of the recognition of Candida albicans by the innate immune system
This paper, published in 2007, received 720 indexed citations . Written by Mihai G. Netea, Gordon D. Brown, Bart Jan Kullberg and Neil A. R. Gow covering the research area of Epidemiology, Immunology and Infectious Diseases. It is primarily cited by scholars working on Infectious Diseases (467 citations), Epidemiology (363 citations) and Immunology (201 citations). Published in Nature Reviews Microbiology.
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/10.1038/nrmicro1815.