Wen Torng
Impact in
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
- Metabolomics and Mass Spectrometry Studies
- RNA and protein synthesis mechanisms
- vaccines and immunoinformatics approaches
Papers in
-
- Protein Structure and Dynamics 3
- Machine Learning in Bioinformatics 2
- Metabolomics and Mass Spectrometry Studies 2
- Genetics, Bioinformatics, and Biomedical Research 1
- RNA and protein synthesis mechanisms 1
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- Computational Drug Discovery Methods 4
- Co-authors
- Russ B. Altman (5 shared papers)Stefano Rensi (1 shared paper)Chau‐Hwang Lee (1 shared paper)P. C. Kuo (1 shared paper)Kung‐Bin Sung (1 shared paper)Matthew Mort (1 shared paper)Gabriele Bassi (1 shared paper)Christian Bock (1 shared paper)
- Journals
- BMC Bioinformatics (1 paper)Bioinformatics (1 paper)ACS Omega (1 paper)Journal of Chemical Information and Modeling (1 paper)PLoS ONE (1 paper)
- Partner nations
- United StatesSwitzerlandTaiwan
In The Last Decade
Wen Torng
7 papers receiving 1.0k citations
Wen Torng's Hit Papers
Peers
Comparison fields: 5 of 115
- Computational Theory and Mathematics 715
- Molecular Biology 632
- Materials Chemistry 337
- Health Informatics 9
- Biophysics 38
Countries citing papers authored by Wen Torng
This map shows the geographic impact of Wen Torng'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 Wen Torng with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Wen Torng more than expected).
Fields of papers citing papers by Wen Torng
This network shows the impact of papers produced by Wen Torng. 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 Wen Torng. The network helps show where Wen Torng may publish in the future.
Co-authors
The 22 scholars most cited alongside Wen Torng, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Machine learning in chemoinformatics and drug discovery Hit paper breakdown → | 2018 | 629 |
| 2 | 2019 | 230 | |
| 3 | 2017 | 100 | |
| 4 | 2018 | 51 | |
| 5 | 2014 | 22 | |
| 6 | 2023 | 9 | |
| 7 | 2017 | 8 |
About Wen Torng
Wen Torng is a scholar working on Molecular Biology, Computational Theory and Mathematics, Biomedical Engineering, Organic Chemistry and Pharmacology, having authored 7 papers that have together received 1.0k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers), Machine Learning in Bioinformatics (2 papers), Metabolomics and Mass Spectrometry Studies (2 papers), Genetics, Bioinformatics, and Biomedical Research (1 paper), Advanced Chemical Sensor Technologies (1 paper), Microbial Natural Products and Biosynthesis (1 paper) and RNA and protein synthesis mechanisms (1 paper). The work is most often cited by research in Computational Theory and Mathematics (715 citations), Molecular Biology (632 citations), Materials Chemistry (337 citations), Health Informatics (9 citations) and Biophysics (38 citations). Wen Torng has collaborated with scholars based in United States, Switzerland and Taiwan. Frequent co-authors include Russ B. Altman, Stefano Rensi, Chau‐Hwang Lee, P. C. Kuo, Kung‐Bin Sung, Matthew Mort, Gabriele Bassi, Christian Bock, Sebastian Oehler and Spencer Bliven. Their work appears in journals such as BMC Bioinformatics, Bioinformatics, ACS Omega, Journal of Chemical Information and Modeling and PLoS ONE.
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.