Debby D. Wang
Impact in
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- Computational Drug Discovery Methods
- Rough Sets and Fuzzy Logic
Papers in
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- Protein Structure and Dynamics 9
- Cancer therapeutics and mechanisms 4
- Machine Learning in Bioinformatics 4
- Gene expression and cancer classification 3
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- Computational Drug Discovery Methods 11
- Co-authors
- Hong Yan (24 shared papers)Victor Lee (7 shared papers)Xizhao Wang (2 shared papers)Yulin He (1 shared paper)Ran Wang (3 shared papers)Lichun Ma (8 shared papers)Haoran Xie (4 shared papers)Maria Pik Wong (4 shared papers)
In The Last Decade
Debby D. Wang
40 papers receiving 657 citations
Peers
Comparison fields: 5 of 107
- Computational Theory and Mathematics 201
- Computational Mathematics 7
- Artificial Intelligence 146
- Molecular Biology 266
- Pulmonary and Respiratory Medicine 110
Countries citing papers authored by Debby D. Wang
This map shows the geographic impact of Debby D. 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 Debby D. Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Debby D. Wang more than expected).
Fields of papers citing papers by Debby D. Wang
This network shows the impact of papers produced by Debby D. 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 Debby D. Wang. The network helps show where Debby D. Wang may publish in the future.
Co-authors
The 25 scholars most cited alongside Debby D. Wang, 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 42 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2013 | 82 | |
| 2 | 2020 | 60 | |
| 3 | 2013 | 60 | |
| 4 | 2020 | 54 | |
| 5 | 2013 | 43 | |
| 6 | 2019 | 37 | |
| 7 | 2017 | 31 | |
| 8 | 2015 | 27 | |
| 9 | 2015 | 25 | |
| 10 | 2024 | 24 | |
| 11 | 2021 | 23 | |
| 12 | 2020 | 18 | |
| 13 | 2021 | 16 | |
| 14 | 2022 | 15 | |
| 15 | 2016 | 15 | |
| 16 | 2023 | 15 | |
| 17 | 2018 | 13 | |
| 18 | 2018 | 12 | |
| 19 | 2014 | 10 | |
| 20 | 2016 | 9 |
About Debby D. Wang
Debby D. Wang is a scholar working on Molecular Biology, Computational Theory and Mathematics, Pulmonary and Respiratory Medicine, Artificial Intelligence and Oncology, having authored 42 papers that have together received 674 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (11 papers), Lung Cancer Treatments and Mutations (10 papers), Protein Structure and Dynamics (9 papers), Colorectal Cancer Treatments and Studies (4 papers), Cancer therapeutics and mechanisms (4 papers), Machine Learning in Bioinformatics (4 papers), Gene expression and cancer classification (3 papers) and Machine Learning in Materials Science (3 papers). The work is most often cited by research in Computational Theory and Mathematics (201 citations), Computational Mathematics (7 citations), Artificial Intelligence (146 citations), Molecular Biology (266 citations) and Pulmonary and Respiratory Medicine (110 citations). Debby D. Wang has collaborated with scholars based in Hong Kong, China and Singapore. Frequent co-authors include Hong Yan, Victor Lee, Xizhao Wang, Yulin He, Ran Wang, Lichun Ma, Haoran Xie, Maria Pik Wong, Le Ou-Yang and Bin Zou. Their work appears in journals such as Computational and Structural Biotechnology Journal, PLoS ONE, IEEE Transactions on Cybernetics, BMC Health Services Research and International Journal of Approximate Reasoning.
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.