Daewoo Pak
Impact in
- Statistics and Probability top 10%
- Statistical Methods and Inference
- Statistical Methods and Bayesian Inference
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- COVID-19 epidemiological studies
Papers in
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- Statistical Methods and Bayesian Inference 4
- Statistical Methods and Inference 4
- Advanced Statistical Methods and Models 3
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- Genomics and Phylogenetic Studies 3
- RNA and protein synthesis mechanisms 3
- RNA modifications and cancer 3
- Co-authors
- Zachary F. Burton (3 shared papers)Robert Root‐Bernstein (1 shared paper)David Todem (3 shared papers)Chenxi Li (3 shared papers)Jing Ning (9 shared papers)Nan Du (1 shared paper)Yanni Sun (1 shared paper)Zhen Lü (2 shared papers)
- Journals
- Transcription (3 papers)Statistics in Medicine (2 papers)Statistical Methods in Medical Research (2 papers)Cancer (1 paper)Scientific Reports (1 paper)
- Partner nations
- United StatesSouth KoreaSpain
In The Last Decade
Daewoo Pak
22 papers receiving 199 citations
Peers
Comparison fields: 5 of 74
- Statistics and Probability 29
- Modeling and Simulation 13
- Cancer Research 23
- Molecular Biology 99
- Reproductive Medicine 12
Countries citing papers authored by Daewoo Pak
This map shows the geographic impact of Daewoo Pak'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 Daewoo Pak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daewoo Pak more than expected).
Fields of papers citing papers by Daewoo Pak
This network shows the impact of papers produced by Daewoo Pak. 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 Daewoo Pak. The network helps show where Daewoo Pak may publish in the future.
Co-authors
The 25 scholars most cited alongside Daewoo Pak, 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 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 28 | |
| 2 | 2017 | 28 | |
| 3 | 2019 | 24 | |
| 4 | 2019 | 22 | |
| 5 | 2018 | 22 | |
| 6 | 2020 | 13 | |
| 7 | 2020 | 9 | |
| 8 | 2018 | 9 | |
| 9 | 2022 | 6 | |
| 10 | 2021 | 6 | |
| 11 | 2012 | 5 | |
| 12 | 2024 | 4 | |
| 13 | 2021 | 4 | |
| 14 | 2016 | 4 | |
| 15 | 2019 | 4 | |
| 16 | 2023 | 3 | |
| 17 | 2021 | 3 | |
| 18 | 2018 | 3 | |
| 19 | 2020 | 2 | |
| 20 | 2023 | 1 |
About Daewoo Pak
Daewoo Pak is a scholar working on Statistics and Probability, Molecular Biology, Physiology, Pulmonary and Respiratory Medicine and Oncology, having authored 24 papers that have together received 202 indexed citations. Recurring topics across this work include Statistical Methods and Bayesian Inference (4 papers), Statistical Methods and Inference (4 papers), Genomics and Phylogenetic Studies (3 papers), Advanced Statistical Methods and Models (3 papers), RNA and protein synthesis mechanisms (3 papers), RNA modifications and cancer (3 papers), Body Composition Measurement Techniques (2 papers) and COVID-19 epidemiological studies (2 papers). The work is most often cited by research in Statistics and Probability (29 citations), Modeling and Simulation (13 citations), Cancer Research (23 citations), Molecular Biology (99 citations) and Reproductive Medicine (12 citations). Daewoo Pak has collaborated with scholars based in United States, South Korea and Spain. Frequent co-authors include Zachary F. Burton, Robert Root‐Bernstein, David Todem, Chenxi Li, Jing Ning, Nan Du, Yanni Sun, Zhen Lü, Yu Shen and Guadalupe Gómez Melis. Their work appears in journals such as Transcription, Statistics in Medicine, Statistical Methods in Medical Research, Cancer and Scientific Reports.
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.