PyClone: statistical inference of clonal population structure in cancer

575 indexed citations

Abstract

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About

This paper, published in 2014, received 575 indexed citations. Written by Andrew Roth, Jaswinder Khattra, Damian Yap, Adrian Wan, Emma Laks, Justina Biele, Gavin Ha, Samuel Aparício, Alexandre Bouchard‐Côté and Sohrab P. Shah covering the research area of Cancer Research, Genetics and Molecular Biology. It is primarily cited by scholars working on Cancer Research (432 citations), Molecular Biology (283 citations) and Oncology (187 citations). Published in Nature Methods.

Countries where authors are citing PyClone: statistical inference of clonal population structure in cancer

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This map shows the geographic impact of PyClone: statistical inference of clonal population structure in cancer. 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 PyClone: statistical inference of clonal population structure in cancer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites PyClone: statistical inference of clonal population structure in cancer more than expected).

Fields of papers citing PyClone: statistical inference of clonal population structure in cancer

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of PyClone: statistical inference of clonal population structure in cancer. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the PyClone: statistical inference of clonal population structure in cancer.

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.2883.

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