Michigan Center for Translational Pathology

231.6k citations
2.9k papers ·

Impact in

    • Cancer-related molecular mechanisms research
    • Cancer Genomics and Diagnostics
    • MicroRNA in disease regulation
  • Oncology top 2%
    • Cancer Cells and Metastasis

Papers in

    • Cancer Genomics and Diagnostics 166
    • Cancer Cells and Metastasis 152
    • Cancer-related Molecular Pathways 89

Michigan Center for Translational Pathology

2.6k papers receiving 203.6k citations

Peers

Michigan Center for Translational Pathology
Comparison fields: 5 of 234
  • Cancer Research 46.4k
  • Oncology 54.0k
  • Immunology 30.5k
  • Molecular Biology 101.1k
  • Pulmonary and Respiratory Medicine 41.3k
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Michigan Center for Translational Pathology relative to Robert H. Lurie Comprehensive Cancer Center of Northwestern University United States Robert H. Lurie Comprehensive Cancer Center of Northwestern University's profile →
Citations per field
00.5×1.6×
Robert H. Lurie Comprehensive Cancer Center of Northwestern University · 1×
Citations per year

Countries citing scholars working at Michigan Center for Translational Pathology

Since Specialization
Citations

This map shows the geographic impact of research produced by authors working at Michigan Center for Translational Pathology. 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 papers produced at Michigan Center for Translational Pathology with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michigan Center for Translational Pathology more than expected).

Fields of papers published by authors at Michigan Center for Translational Pathology

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers affiliated with Michigan Center for Translational Pathology at the time of their publication. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers affiliated with Michigan Center for Translational Pathology at the time of their publication.

About Michigan Center for Translational Pathology

In recent decades, authors affiliated with Michigan Center for Translational Pathology have published 2.9k papers, which have received a total of 231.6k indexed citations . Scholars at this organization have produced 474 papers in Cancer Research, 758 papers in Oncology, 204 papers in Hematology, 492 papers in Pulmonary and Respiratory Medicine and 1.1k papers in Molecular Biology on the topics of Prostate Cancer Treatment and Research (219 papers), Cancer Genomics and Diagnostics (166 papers), Cancer Cells and Metastasis (152 papers), Epigenetics and DNA Methylation (111 papers), Immunotherapy and Immune Responses (95 papers), Ubiquitin and proteasome pathways (91 papers), Cancer-related Molecular Pathways (89 papers) and Prostate Cancer Diagnosis and Treatment (89 papers). Their work is cited by papers focused on Cancer Research (46.4k citations), Oncology (54.0k citations), Immunology (30.5k citations), Molecular Biology (101.1k citations) and Pulmonary and Respiratory Medicine (41.3k citations). Authors at Michigan Center for Translational Pathology collaborate with scholars in United States, China and Canada and have published in prestigious journals including Blood, Cancer Research, Clinical Cancer Research, Neoplasia and Cancer. Some of Michigan Center for Translational Pathology's most productive authors include Gabriel Núñez, Arul M. Chinnaiyan, Max S. Wicha, Naohiro Inohara, Daniel F.C. Hayes, Luigi Franchi, Kenneth J. Pienta, Scott Arthur Tomlins, John R. Prensner and Raúl Muñoz-Planillo.

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.

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