A Big Bang model of human colorectal tumor growth

666 indexed citations

Abstract

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About

This paper, published in 2015, received 666 indexed citations. Written by Andrea Sottoriva, Haeyoun Kang, Zhicheng Ma, Trevor A. Graham, Matthew P. Salomon, Junsong Zhao, Paul Marjoram, Kimberly D. Siegmund, Michael F. Press and Darryl Shibata covering the research area of Cancer Research, Pathology and Forensic Medicine and Modeling and Simulation. It is primarily cited by scholars working on Cancer Research (468 citations), Oncology (291 citations) and Molecular Biology (252 citations). Published in Nature Genetics.

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Countries where authors are citing A Big Bang model of human colorectal tumor growth

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This map shows the geographic impact of A Big Bang model of human colorectal tumor growth. 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 A Big Bang model of human colorectal tumor growth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A Big Bang model of human colorectal tumor growth more than expected).

Fields of papers citing A Big Bang model of human colorectal tumor growth

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of A Big Bang model of human colorectal tumor growth. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the A Big Bang model of human colorectal tumor growth.

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/ng.3214.

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