Su‐In Lee
- Health Informatics top 0.1%
- Artificial Intelligence in Healthcare and Education 5
- Artificial Intelligence top 0.5%
- Machine Learning in Healthcare 9
- Explainable Artificial Intelligence (XAI) 9
- Health Information Management top 0.5%
- Environmental Engineering top 2%
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- Gene expression and cancer classification 13
- Bioinformatics and Genomic Networks 9
- Single-cell and spatial transcriptomics 6
- Genomics and Chromatin Dynamics 6
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- Cell Image Analysis Techniques 4
Su‐In Lee
63 papers receiving 8.5k citations
Hit Papers
Peers
Comparison fields: 5 of 222
- Health Informatics 470
- Artificial Intelligence 2.1k
- Health Information Management 292
- Environmental Engineering 532
- Radiology, Nuclear Medicine and Imaging 727
Countries citing papers authored by Su‐In Lee
This map shows the geographic impact of Su‐In Lee'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 Su‐In Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Su‐In Lee more than expected).
Fields of papers citing papers by Su‐In Lee
This network shows the impact of papers produced by Su‐In Lee. 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 Su‐In Lee. The network helps show where Su‐In Lee may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Su‐In Lee, 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 | 2025 | 2 | |
| 2 | 2024 | 2 | |
| 3 | 2024 | 36 | |
| 4 | 2023 | 27 | |
| 5 | 2023 | 18 | |
| 6 | 2023 | 11 | |
| 7 | 2022 | 18 | |
| 8 | 2021 | 33 | |
| 9 | Improving KernelSHAP: Practical Shapley Value Estimation Using Linear Regression | 2021 | 30 |
| 10 | 2021 | 13 | |
| 11 | 2020 | 35 | |
| 12 | From local explanations to global understanding with explainable AI for treesbreakdown → | 2020 | 4764 |
| 13 | Understanding Global Feature Contributions With Additive Importance Measures | 2020 | 6 |
| 14 | Automated detection of glaucoma using retinal images with interpretable deep learning | 2020 | 4 |
| 15 | 2018 | 6 | |
| 16 | 2017 | 192 | |
| 17 | Learning Sparse Gaussian Graphical Models with Overlapping Blocks | 2016 | 12 |
| 18 | Efficient Dimensionality Reduction for High-Dimensional Network Estimation | 2014 | 12 |
| 19 | 2013 | 8 | |
| 20 | Postmodernism Expressions in Contemporary Hairstyle in Collections(I) | 2006 | 1 |
About Su‐In Lee
Su‐In Lee is a scholar working on Health Informatics, Anatomy, Biophysics, Artificial Intelligence and Medical Laboratory Technology, having authored 65 papers that have together received 8.7k indexed citations. Recurring topics across this work include Gene expression and cancer classification (13 papers), Machine Learning in Healthcare (9 papers), Explainable Artificial Intelligence (XAI) (9 papers), Bioinformatics and Genomic Networks (9 papers), Single-cell and spatial transcriptomics (6 papers), Genomics and Chromatin Dynamics (6 papers), Artificial Intelligence in Healthcare and Education (5 papers) and Cell Image Analysis Techniques (4 papers). The work is most often cited by research in Health Informatics (470 citations), Artificial Intelligence (2.1k citations), Health Information Management (292 citations), Environmental Engineering (532 citations) and Radiology, Nuclear Medicine and Imaging (727 citations). Su‐In Lee has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Scott Lundberg, Hugh Chen, Bala G. Nair, Alex J. DeGrave, Gabriel Erion, Jordan M. Prutkin, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal and Joseph D. Janizek. Their work appears in journals such as Nature Biomedical Engineering, Nature Communications, Genome biology, Nature Machine Intelligence and Blood.
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.