Mingjun Yang

1.4k citations
41 papers · 1.1k · h-index 20

Impact in

Papers in

Mingjun Yang

39 papers receiving 1.1k citations

Peers

Mingjun Yang
Comparison fields: 5 of 116
  • Biomaterials 197
  • Physical and Theoretical Chemistry 88
  • Computational Theory and Mathematics 151
  • Endocrinology 37
  • Molecular Biology 472
Replace Nathan R. Kern with:
Nathan R. Kern United States
Roland G. Huber Singapore
Franci Merzel Slovenia
Lukas D. Schuler Switzerland
Hannes Fischer Brazil
Nicholas C. Fitzkee United States
Noriyuki Igarashi Japan
Mary T. McBride United States
Sumati Bhatia Germany
Jesse B. Hopkins United States
Mingjun Yang relative to Nathan R. Kern United States Nathan R. Kern's profile →
Citations per field
00.5×10×20×31×
Nathan R. Kern · 1×
Citations per year

Countries citing papers authored by Mingjun Yang

Since Specialization
Citations

This map shows the geographic impact of Mingjun Yang'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 Mingjun Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingjun Yang more than expected).

Fields of papers citing papers by Mingjun Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mingjun Yang. 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 Mingjun Yang. The network helps show where Mingjun Yang may publish in the future.

Co-authors

The 25 scholars most cited alongside Mingjun Yang, 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 Mingjun Yang Line = papers co-authored together Mingjun Yang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2008124
2 2010103
3 201565
4 202059
5 202354
6 201849
7 201748
8 201046
9 201540
10 201737
11 201537
12 202037
13 202135
14 201835
15 201433
16 201632
17 200931
18 201427
19 201927
20 202025

About Mingjun Yang

Mingjun Yang is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Spectroscopy and Physical and Theoretical Chemistry, having authored 41 papers that have together received 1.1k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (17 papers), Computational Drug Discovery Methods (7 papers), Enzyme Structure and Function (6 papers), Glycosylation and Glycoproteins Research (4 papers), Machine Learning in Materials Science (4 papers), Spectroscopy and Quantum Chemical Studies (4 papers), Crystallography and molecular interactions (3 papers) and RNA and protein synthesis mechanisms (3 papers). The work is most often cited by research in Biomaterials (197 citations), Physical and Theoretical Chemistry (88 citations), Computational Theory and Mathematics (151 citations), Endocrinology (37 citations) and Molecular Biology (472 citations). Mingjun Yang has collaborated with scholars based in United States, China and Denmark. Frequent co-authors include Alexander D. MacKerell, S. L. S. Stipp, John H. Harding, Keli Han, Kenno Vanommeslaeghe, Jing Huang, Asaminew H. Aytenfisu, DJ Cook, Peiyu Zhang and E. Makovicky. Their work appears in journals such as Journal of Chemical Theory and Computation, Crystal Growth & Design, The Journal of Chemical Physics, Journal of Chemical Information and Modeling and Proteins Structure Function and Bioinformatics.

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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