Analysis of the coding genome of diffuse large B-cell lymphoma

Abstract

loading...

About

This paper, published in 1950, received 672 indexed citations. Written by Laura Pasqualucci, Владимир Трифонов, Giulia Fabbri, Jing Ma, Davide Rossi, Annalisa Chiarenza, Victoria A. Wells, Adina Grunn, Monica Messina and Joseph M. Chan covering the research area of Pathology and Forensic Medicine, Immunology and Genetics. It is primarily cited by scholars working on Pathology and Forensic Medicine (406 citations), Molecular Biology (303 citations) and Genetics (214 citations). Published in Nature Genetics.

In The Last Decade

doi.org/10.1038/ng.892 →

Countries where authors are citing Analysis of the coding genome of diffuse large B-cell lymphoma

Since Specialization
Citations

This map shows the geographic impact of Analysis of the coding genome of diffuse large B-cell lymphoma. 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 Analysis of the coding genome of diffuse large B-cell lymphoma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Analysis of the coding genome of diffuse large B-cell lymphoma more than expected).

Fields of papers citing Analysis of the coding genome of diffuse large B-cell lymphoma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Analysis of the coding genome of diffuse large B-cell lymphoma. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Analysis of the coding genome of diffuse large B-cell lymphoma.

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.892.

Explore hit-papers with similar magnitude of impact

Rankless by CCL
2026