Ming Ji
Impact in
- Cancer Research top 5%
- MicroRNA in disease regulation
- Cancer-related molecular mechanisms research
- Cancer, Hypoxia, and Metabolism
- Cell Biology top 5%
- Hippo pathway signaling and YAP/TAZ
Papers in
-
- RNA modifications and cancer 5
- Epigenetics and DNA Methylation 4
- Oncology 15
- Peptidase Inhibition and Analysis 4
- Co-authors
- Yuanhong Chen (5 shared papers)Jixin Dong (5 shared papers)Ling Xiao (4 shared papers)Lois Biener (1 shared paper)Alison B. Albers (1 shared paper)Elizabeth A. Gilpin (1 shared paper)Xiaoling Li (6 shared papers)Jonathan R. Keller (3 shared papers)
- Journals
- Journal of Biological Chemistry (5 papers)Blood (3 papers)Journal of Medicinal Chemistry (3 papers)PLoS ONE (3 papers)OncoTargets and Therapy (2 papers)
- Partner nations
- ChinaUnited StatesUnited Kingdom
In The Last Decade
Ming Ji
89 papers receiving 2.2k citations
Peers
Comparison fields: 5 of 149
- Cancer Research 406
- Cell Biology 349
- Applied Psychology 97
- Molecular Biology 1.1k
- Geriatrics and Gerontology 48
Countries citing papers authored by Ming Ji
This map shows the geographic impact of Ming Ji'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 Ming Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Ji more than expected).
Fields of papers citing papers by Ming Ji
This network shows the impact of papers produced by Ming Ji. 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 Ming Ji. The network helps show where Ming Ji may publish in the future.
Co-authors
The 25 scholars most cited alongside Ming Ji, 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 | 2004 | 163 | |
| 2 | 2018 | 158 | |
| 3 | 2011 | 158 | |
| 4 | 2011 | 145 | |
| 5 | 2020 | 78 | |
| 6 | 2013 | 78 | |
| 7 | 2002 | 75 | |
| 8 | 2014 | 71 | |
| 9 | 2008 | 60 | |
| 10 | 2001 | 59 | |
| 11 | 2011 | 58 | |
| 12 | 2022 | 50 | |
| 13 | 2011 | 49 | |
| 14 | 2010 | 48 | |
| 15 | 2017 | 46 | |
| 16 | 2008 | 45 | |
| 17 | 2018 | 43 | |
| 18 | 2012 | 42 | |
| 19 | 2013 | 42 | |
| 20 | 2004 | 40 |
About Ming Ji
Ming Ji is a scholar working on Molecular Biology, Oncology, Cancer Research, Pulmonary and Respiratory Medicine and Cell Biology, having authored 92 papers that have together received 2.3k indexed citations. Recurring topics across this work include Cancer, Hypoxia, and Metabolism (7 papers), Cancer-related molecular mechanisms research (6 papers), Microtubule and mitosis dynamics (5 papers), RNA modifications and cancer (5 papers), Hippo pathway signaling and YAP/TAZ (5 papers), Epigenetics and DNA Methylation (4 papers), Nanoplatforms for cancer theranostics (4 papers) and Peptidase Inhibition and Analysis (4 papers). The work is most often cited by research in Cancer Research (406 citations), Cell Biology (349 citations), Applied Psychology (97 citations), Molecular Biology (1.1k citations) and Geriatrics and Gerontology (48 citations). Ming Ji has collaborated with scholars based in China, United States and United Kingdom. Frequent co-authors include Yuanhong Chen, Jixin Dong, Ling Xiao, Lois Biener, Alison B. Albers, Elizabeth A. Gilpin, Xiaoling Li, Jonathan R. Keller, Kimberly D. Klarmann and Wing C. Chan. Their work appears in journals such as Journal of Biological Chemistry, Blood, Journal of Medicinal Chemistry, PLoS ONE and OncoTargets and Therapy.
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.