Jason Yim
Impact in
Papers in
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- RNA and protein synthesis mechanisms 1
- Biomedical Text Mining and Ontologies 1
- Cancer-related gene regulation 1
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- Machine Learning and Data Classification 1
- Topic Modeling 1
- Co-authors
- Regina Barzilay (3 shared papers)H. Stärk (1 shared paper)Gabriele Corso (1 shared paper)Bowen Jing (1 shared paper)Tommi Jaakkola (2 shared papers)David Baker (2 shared papers)Yakov Kipnis (1 shared paper)Woody Ahern (2 shared papers)
- Journals
- Nature Methods (1 paper)Wiley Interdisciplinary Reviews Computational Molecular Science (1 paper)Investigative Ophthalmology & Visual Science (1 paper)Nature (1 paper)PubMed (1 paper)
- Partner nations
- United StatesSwitzerlandUnited Kingdom
In The Last Decade
Jason Yim
6 papers receiving 52 citations
Peers
Comparison fields: 5 of 21
- Health Informatics 1
- Structural Biology 1
- Computational Theory and Mathematics 10
- Molecular Biology 23
- Complementary and alternative medicine 2
Countries citing papers authored by Jason Yim
This map shows the geographic impact of Jason Yim'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 Jason Yim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jason Yim more than expected).
Fields of papers citing papers by Jason Yim
This network shows the impact of papers produced by Jason Yim. 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 Jason Yim. The network helps show where Jason Yim may publish in the future.
Co-authors
The 25 scholars most cited alongside Jason Yim, 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 | 2024 | 33 | |
| 2 | 2025 | 8 | |
| 3 | 2024 | 5 | |
| 4 | 2025 | 3 | |
| 5 | Random Projection Forests | 2015 | 2 |
| 6 | Quantitative analysis of change in retinal tissues in neovascular age-related macular degeneration using artificial intelligence | 2020 | 1 |
About Jason Yim
Jason Yim is a scholar working on Molecular Biology, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Environmental Engineering and Genetics, having authored 6 papers that have together received 52 indexed citations. Recurring topics across this work include Hydrological Forecasting Using AI (1 paper), RNA and protein synthesis mechanisms (1 paper), Retinal Imaging and Analysis (1 paper), Machine Learning and Data Classification (1 paper), Biomedical Text Mining and Ontologies (1 paper), Cancer-related gene regulation (1 paper), Bacterial Genetics and Biotechnology (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Health Informatics (1 citation), Structural Biology (1 citation), Computational Theory and Mathematics (10 citations), Molecular Biology (23 citations) and Complementary and alternative medicine (2 citations). Jason Yim has collaborated with scholars based in United States, Switzerland and United Kingdom. Frequent co-authors include Regina Barzilay, H. Stärk, Gabriele Corso, Bowen Jing, Tommi Jaakkola, David Baker, Yakov Kipnis, Woody Ahern, Andrew T. Campbell and Rohith Krishna. Their work appears in journals such as Nature Methods, Wiley Interdisciplinary Reviews Computational Molecular Science, Investigative Ophthalmology & Visual Science, Nature and PubMed.
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.