MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis

2.3k indexed citations

Abstract

loading...

About

This paper, published in 2015, received 2.3k indexed citations. Written by Hiroshi Tsugawa, Tomáš Čajka, Tobias Kind, Yan Ma, Brendan T. Higgins, Kazutaka Ikeda, Mitsuhiro Kanazawa, Jean S. VanderGheynst, Oliver Fiehn and Masanori Arita covering the research area of Molecular Biology, Ecology and Spectroscopy. It is primarily cited by scholars working on Molecular Biology (1.6k citations), Spectroscopy (434 citations) and Plant Science (320 citations). Published in Nature Methods.

Countries where authors are citing MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis

Specialization
Citations

This map shows the geographic impact of MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. 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 MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis more than expected).

Fields of papers citing MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis

Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis.

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/nmeth.3393.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026