Dedi Wang

419 citations
9 papers · 224 · h-index 7

Impact in

Papers in

    • Protein Structure and Dynamics 8
    • Bioinformatics and Genomic Networks 1
    • Machine Learning in Bioinformatics 1
    • Machine Learning in Materials Science 4

Dedi Wang

9 papers receiving 224 citations

Peers

Dedi Wang
Comparison fields: 5 of 43
  • Computational Theory and Mathematics 63
  • Molecular Biology 171
  • Materials Chemistry 104
  • Spectroscopy 24
  • Structural Biology 2
Replace Jakub Rydzewski with:
Jakub Rydzewski Poland
Zachary Smith United States
Dominik Lemm Germany
Olivier Adjoua France
George Boxer United States
Adam J. Pratt United States
Hannah M. Baumann United States
Brian Olson United States
Sergey N. Pozdnyakov Switzerland
Junhan Chang China
Dedi Wang relative to Jakub Rydzewski Poland Jakub Rydzewski's profile →
Citations per field
00.5×
Jakub Rydzewski · 1×
Citations per year

Countries citing papers authored by Dedi Wang

Since Specialization
Citations

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

Fields of papers citing papers by Dedi Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 5 scholars most cited alongside Dedi Wang, 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 Dedi Wang Line = papers co-authored together Dedi Wang links everyone, so they are left out of the graph.

All Works

9 of 9 papers shown
#Work
1 202172
2 202358
3 202239
4 202516
5 202212
6 20248
7 20248
8 20246
9 20245

About Dedi Wang

Dedi Wang is a scholar working on Molecular Biology, Materials Chemistry, Computational Theory and Mathematics, Atomic and Molecular Physics, and Optics and Statistical and Nonlinear Physics, having authored 9 papers that have together received 224 indexed citations. Recurring topics across this work include Protein Structure and Dynamics (8 papers), Machine Learning in Materials Science (4 papers), Computational Drug Discovery Methods (3 papers), Spectroscopy and Quantum Chemical Studies (2 papers), Bioinformatics and Genomic Networks (1 paper), Electrostatics and Colloid Interactions (1 paper), Machine Learning in Bioinformatics (1 paper) and Mass Spectrometry Techniques and Applications (1 paper). The work is most often cited by research in Computational Theory and Mathematics (63 citations), Molecular Biology (171 citations), Materials Chemistry (104 citations), Spectroscopy (24 citations) and Structural Biology (2 citations). Dedi Wang has collaborated with scholars based in United States. Frequent co-authors include Pratyush Tiwary, Shashank Pant, John D. Weeks, Yihang Wang and Markus A. Seeliger. Their work appears in journals such as Journal of Chemical Theory and Computation, The Journal of Chemical Physics, Current Opinion in Structural Biology, The Journal of Physical Chemistry B and Digital Discovery.

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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