Jun S. Wei
Impact in
- Cancer Research top 1%
- Cancer-related molecular mechanisms research
- MicroRNA in disease regulation
- Cancer, Hypoxia, and Metabolism
- Neurology top 1%
- Neuroblastoma Research and Treatments
Papers in
- Neurology 31
- Neuroblastoma Research and Treatments 26
- Co-authors
- Javed Khan (56 shared papers)Frank Westermann (7 shared papers)Manfred Schwab (6 shared papers)Frank Berthold (4 shared papers)Marc Ladanyi (2 shared papers)Markus Ringnér (1 shared paper)Cristina R. Antonescu (1 shared paper)Lao H. Saal (1 shared paper)
- Journals
- Cancer Research (7 papers)Clinical Cancer Research (6 papers)Oncogene (4 papers)PLoS ONE (4 papers)Nature Communications (3 papers)
- Partner nations
- United StatesChinaGermany
In The Last Decade
Jun S. Wei
90 papers receiving 5.2k citations
Jun S. Wei's Hit Papers
Peers
Comparison fields: 5 of 162
- Cancer Research 1.1k
- Neurology 887
- Molecular Biology 3.4k
- Endocrinology 211
- Oncology 730
Countries citing papers authored by Jun S. Wei
This map shows the geographic impact of Jun S. Wei'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 Jun S. Wei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun S. Wei more than expected).
Fields of papers citing papers by Jun S. Wei
This network shows the impact of papers produced by Jun S. Wei. 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 Jun S. Wei. The network helps show where Jun S. Wei may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun S. Wei, 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 92 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks Hit paper breakdown → | 2001 | 1919 |
| 2 | 2003 | 318 | |
| 3 | 2008 | 232 | |
| 4 | 2001 | 155 | |
| 5 | 2004 | 141 | |
| 6 | 2011 | 140 | |
| 7 | 2015 | 120 | |
| 8 | 2021 | 106 | |
| 9 | 2005 | 102 | |
| 10 | 2007 | 91 | |
| 11 | 2009 | 81 | |
| 12 | 2017 | 79 | |
| 13 | 2018 | 76 | |
| 14 | 2012 | 76 | |
| 15 | 2013 | 74 | |
| 16 | 2010 | 73 | |
| 17 | 2017 | 72 | |
| 18 | 2004 | 72 | |
| 19 | 2019 | 69 | |
| 20 | 2004 | 56 |
About Jun S. Wei
Jun S. Wei is a scholar working on Molecular Biology, Neurology, Cancer Research, Pulmonary and Respiratory Medicine and Oncology, having authored 92 papers that have together received 5.4k indexed citations. Recurring topics across this work include Neuroblastoma Research and Treatments (26 papers), Sarcoma Diagnosis and Treatment (11 papers), Cancer, Hypoxia, and Metabolism (11 papers), CAR-T cell therapy research (9 papers), MicroRNA in disease regulation (8 papers), Immunotherapy and Immune Responses (7 papers), Cancer-related molecular mechanisms research (7 papers) and Occupational and environmental lung diseases (6 papers). The work is most often cited by research in Cancer Research (1.1k citations), Neurology (887 citations), Molecular Biology (3.4k citations), Endocrinology (211 citations) and Oncology (730 citations). Jun S. Wei has collaborated with scholars based in United States, China and Germany. Frequent co-authors include Javed Khan, Frank Westermann, Manfred Schwab, Frank Berthold, Marc Ladanyi, Markus Ringnér, Cristina R. Antonescu, Lao H. Saal, Carsten Peterson and Paul S. Meltzer. Their work appears in journals such as Cancer Research, Clinical Cancer Research, Oncogene, PLoS ONE and Nature Communications.
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.