Ming Lin
Impact in
- Cancer Research top 10%
- Protease and Inhibitor Mechanisms
- Immunology and Allergy top 10%
- Cell Adhesion Molecules Research
Papers in
-
- Ubiquitin and proteasome pathways 6
- Protein Structure and Dynamics 4
- DNA Repair Mechanisms 3
- Oncology 8
- Co-authors
- Lynne T. Smith (1 shared paper)Renato V. Iozzo (1 shared paper)Steven D. Bain (1 shared paper)Mary E. LaMarca (1 shared paper)Paul Börnstein (1 shared paper)Edward I. Ginns (1 shared paper)Themis R. Kyriakides (1 shared paper)Zhantao Yang (1 shared paper)
- Journals
- The Journal of Chemical Physics (4 papers)Journal of the American Statistical Association (3 papers)Blood (2 papers)Cancer Research (2 papers)The Journal of Cell Biology (2 papers)
- Partner nations
- ChinaUnited StatesTaiwan
In The Last Decade
Ming Lin
47 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 131
- Cancer Research 180
- Immunology and Allergy 71
- Molecular Biology 719
- Oncology 213
- Cell Biology 119
Countries citing papers authored by Ming Lin
This map shows the geographic impact of Ming Lin'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 Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Lin more than expected).
Fields of papers citing papers by Ming Lin
This network shows the impact of papers produced by Ming Lin. 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 Lin. The network helps show where Ming Lin may publish in the future.
Co-authors
The 25 scholars most cited alongside Ming Lin, 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 50 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 1998 | 442 | |
| 2 | 2019 | 66 | |
| 3 | 2009 | 61 | |
| 4 | 2013 | 59 | |
| 5 | 2010 | 49 | |
| 6 | 2009 | 45 | |
| 7 | 2008 | 45 | |
| 8 | [Identification and characterization of LAPTM4B encoded by a human hepatocellular carcinoma-associated novel gene]. | 2003 | 41 |
| 9 | 2015 | 40 | |
| 10 | 2020 | 40 | |
| 11 | 2014 | 34 | |
| 12 | 2015 | 32 | |
| 13 | 2012 | 32 | |
| 14 | 1999 | 29 | |
| 15 | 2013 | 29 | |
| 16 | 2005 | 28 | |
| 17 | 2010 | 26 | |
| 18 | 2017 | 23 | |
| 19 | 2011 | 22 | |
| 20 | 2010 | 21 |
About Ming Lin
Ming Lin is a scholar working on Molecular Biology, Oncology, Statistics and Probability, Immunology and Artificial Intelligence, having authored 50 papers that have together received 1.4k indexed citations. Recurring topics across this work include Ubiquitin and proteasome pathways (6 papers), Statistical Methods and Inference (6 papers), Immunotherapy and Immune Responses (4 papers), Advanced Causal Inference Techniques (4 papers), Protein Structure and Dynamics (4 papers), Cancer Research and Treatments (4 papers), DNA Repair Mechanisms (3 papers) and Target Tracking and Data Fusion in Sensor Networks (3 papers). The work is most often cited by research in Cancer Research (180 citations), Immunology and Allergy (71 citations), Molecular Biology (719 citations), Oncology (213 citations) and Cell Biology (119 citations). Ming Lin has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Lynne T. Smith, Renato V. Iozzo, Steven D. Bain, Mary E. LaMarca, Paul Börnstein, Edward I. Ginns, Themis R. Kyriakides, Zhantao Yang, Keith G. Danielson and Cindy E. McKinney. Their work appears in journals such as The Journal of Chemical Physics, Journal of the American Statistical Association, Blood, Cancer Research and The Journal of Cell Biology.
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.