Tomi Jun
Impact in
- Hepatology top 10%
- Hepatitis C virus research
- Infectious Diseases top 10%
- COVID-19 Clinical Research Studies
- SARS-CoV-2 and COVID-19 Research
Papers in
- Oncology 16
- Cancer Immunotherapy and Biomarkers 7
- PARP inhibition in cancer therapy 4
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- Prostate Cancer Treatment and Research 6
- Co-authors
- Kuan‐lin Huang (7 shared papers)Mindie H. Nguyen (4 shared papers)Leonard Naymagon (2 shared papers)Ruma Rajbhandari (1 shared paper)Douglas Tremblay (3 shared papers)Raymond T. Chung (1 shared paper)Ashwin N. Ananthakrishnan (1 shared paper)Jonathan Feld (3 shared papers)
- Journals
- Journal of Clinical Oncology (8 papers)The Oncologist (2 papers)Scientific Reports (2 papers)JCO Clinical Cancer Informatics (2 papers)Annals of Oncology (2 papers)
- Partner nations
- United StatesTaiwanUnited Kingdom
In The Last Decade
Tomi Jun
49 papers receiving 546 citations
Peers
Comparison fields: 5 of 79
- Hepatology 74
- Infectious Diseases 115
- Internal Medicine 15
- Epidemiology 110
- Oncology 83
Countries citing papers authored by Tomi Jun
This map shows the geographic impact of Tomi Jun's research. 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 Tomi Jun with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomi Jun more than expected).
Fields of papers citing papers by Tomi Jun
This network shows the impact of papers produced by Tomi Jun. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tomi Jun. The network helps show where Tomi Jun may publish in the future.
Co-authors
The 25 scholars most cited alongside Tomi Jun, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 54 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 85 | |
| 2 | 2016 | 35 | |
| 3 | 2020 | 33 | |
| 4 | 2019 | 32 | |
| 5 | 2018 | 31 | |
| 6 | 2021 | 30 | |
| 7 | 2018 | 29 | |
| 8 | 2019 | 28 | |
| 9 | 2017 | 25 | |
| 10 | 2021 | 23 | |
| 11 | 2022 | 18 | |
| 12 | 2021 | 15 | |
| 13 | 2017 | 14 | |
| 14 | 2018 | 13 | |
| 15 | 2021 | 12 | |
| 16 | 2019 | 12 | |
| 17 | 2024 | 10 | |
| 18 | 2014 | 10 | |
| 19 | 2017 | 10 | |
| 20 | 2023 | 9 |
About Tomi Jun
Tomi Jun is a scholar working on Oncology, Pulmonary and Respiratory Medicine, Epidemiology, Infectious Diseases and Hepatology, having authored 54 papers that have together received 555 indexed citations. Recurring topics across this work include Cancer Immunotherapy and Biomarkers (7 papers), Prostate Cancer Treatment and Research (6 papers), COVID-19 Clinical Research Studies (5 papers), Cancer Genomics and Diagnostics (5 papers), Hepatocellular Carcinoma Treatment and Prognosis (4 papers), Bladder and Urothelial Cancer Treatments (4 papers), Liver Disease Diagnosis and Treatment (4 papers) and PARP inhibition in cancer therapy (4 papers). The work is most often cited by research in Hepatology (74 citations), Infectious Diseases (115 citations), Internal Medicine (15 citations), Epidemiology (110 citations) and Oncology (83 citations). Tomi Jun has collaborated with scholars based in United States, Taiwan and United Kingdom. Frequent co-authors include Kuan‐lin Huang, Mindie H. Nguyen, Leonard Naymagon, Ruma Rajbhandari, Douglas Tremblay, Raymond T. Chung, Ashwin N. Ananthakrishnan, Jonathan Feld, Hamed Khalili and Ming‐Lun Yeh. Their work appears in journals such as Journal of Clinical Oncology, The Oncologist, Scientific Reports, JCO Clinical Cancer Informatics and Annals of Oncology.
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.