Ming Lin

1.7k citations
51 papers · 1.3k · h-index 20

Impact in

Papers in

    • Ubiquitin and proteasome pathways 9
    • Protein Structure and Dynamics 4
    • Glycosylation and Glycoproteins Research 4
    • Cancer Immunotherapy and Biomarkers 3

Ming Lin

47 papers receiving 1.3k citations

Peers

Ming Lin
Comparison fields: 5 of 132
  • Cancer Research 217
  • Immunology and Allergy 69
  • Molecular Biology 778
  • Oncology 235
  • Cell Biology 125
Replace Milton W. Datta with:
Milton W. Datta United States
A Peng China
Paul Elvin United Kingdom
Tim H. Brümmendorf Germany
Sameer Jhavar United States
Håvard E. Danielsen Norway
Heidi S. Erickson United States
Rakesh Heer United Kingdom
Ole J. Halvorsen Norway
Ai-Min Hui United States
Ming Lin relative to Milton W. Datta United States Milton W. Datta's profile →
Citations per field
00.5×1.7×
Milton W. Datta · 1×
Citations per year

Countries citing papers authored by Ming Lin

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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.

Border = papers with Ming Lin Line = papers co-authored together Ming Lin links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 51 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1998407
2 201960
3 200959
4 201357
5 201048
6 200844
7 200942
8
[Identification and characterization of LAPTM4B encoded by a human hepatocellular carcinoma-associated novel gene].
200340
9 202038
10 201537
11 201432
12 201532
13 201232
14 199928
15 201327
16 201024
17 200524
18 201723
19 201122
20 200620

About Ming Lin

Ming Lin is a scholar working on Molecular Biology, Oncology, Statistics and Probability, Immunology and Cancer Research, having authored 51 papers that have together received 1.3k indexed citations. Recurring topics across this work include Ubiquitin and proteasome pathways (9 papers), Statistical Methods and Inference (6 papers), Cancer Research and Treatments (4 papers), Protein Structure and Dynamics (4 papers), Immunotherapy and Immune Responses (4 papers), Glycosylation and Glycoproteins Research (4 papers), Advanced Causal Inference Techniques (4 papers) and Cancer Immunotherapy and Biomarkers (3 papers). The work is most often cited by research in Cancer Research (217 citations), Immunology and Allergy (69 citations), Molecular Biology (778 citations), Oncology (235 citations) and Cell Biology (125 citations). Ming Lin has collaborated with scholars based in China, United States and Taiwan. Frequent co-authors include Themis R. Kyriakides, Steven D. Bain, Zhantao Yang, Lynne T. Smith, Paul Börnstein, Cindy E. McKinney, Mary E. LaMarca, Keith G. Danielson, Edward I. Ginns and Renato V. Iozzo. Their work appears in journals such as The Journal of Chemical Physics, Journal of the American Statistical Association, Cancer Research, The Journal of Cell Biology and Journal of Biomedical Science.

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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