Reversed graph embedding resolves complex single-cell trajectories

2.8k indexed citations
published 2017

Countries where authors are citing Reversed graph embedding resolves complex single-cell trajectories

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Citations

This map shows the geographic impact of Reversed graph embedding resolves complex single-cell trajectories. 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 Reversed graph embedding resolves complex single-cell trajectories with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Reversed graph embedding resolves complex single-cell trajectories more than expected).

Fields of papers citing Reversed graph embedding resolves complex single-cell trajectories

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Reversed graph embedding resolves complex single-cell trajectories. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Reversed graph embedding resolves complex single-cell trajectories.

About Reversed graph embedding resolves complex single-cell trajectories

This paper, published in 2017, received 2.8k indexed citations . Written by Xiaojie Qiu, Qi Mao, Ying Tang, Li Wang, Raghav Chawla, Hannah A. Pliner and Cole Trapnell covering the research area of Molecular Biology and Cancer Research. It is primarily cited by scholars working on Molecular Biology (1.6k citations), Immunology (693 citations), Cancer Research (373 citations), Oncology (331 citations) and Pulmonary and Respiratory Medicine (210 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/nmeth.4402.

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