Microarray-based, high-throughput gene expression profiling of microRNAs
Impact in
- Cancer Research 351
Classified as
- Journal
- Nature Methods
In The Last Decade
doi.org/10.1038/nmeth717 →Countries where authors are citing Microarray-based, high-throughput gene expression profiling of microRNAs
This map shows the geographic impact of Microarray-based, high-throughput gene expression profiling of microRNAs. 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 Microarray-based, high-throughput gene expression profiling of microRNAs with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Microarray-based, high-throughput gene expression profiling of microRNAs more than expected).
Fields of papers citing Microarray-based, high-throughput gene expression profiling of microRNAs
This network shows the impact of Microarray-based, high-throughput gene expression profiling of microRNAs. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Microarray-based, high-throughput gene expression profiling of microRNAs.
About Microarray-based, high-throughput gene expression profiling of microRNAs
This paper, published in 2004, received 520 indexed citations . Written by Peter T. Nelson, Don A. Baldwin, L. Marie Scearce, J. Carl Oberholtzer, John W. Tobias and Zissimos P. Mourelatos covering the research area of Cancer Research and Molecular Biology. It is primarily cited by scholars working on Molecular Biology (444 citations), Cancer Research (351 citations), Biomedical Engineering (43 citations), Materials Chemistry (23 citations) and Plant Science (14 citations). Published in Nature Methods.
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/nmeth717.