Ingoo Lee
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
- Genetics, Bioinformatics, and Biomedical Research
- vaccines and immunoinformatics approaches
Papers in
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- Computational Drug Discovery Methods 5
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- Machine Learning in Bioinformatics 2
- Protein Structure and Dynamics 2
- Biochemical and Structural Characterization 1
- Biomedical Text Mining and Ontologies 1
- Gene expression and cancer classification 1
- Co-authors
- Hojung Nam (7 shared papers)Hansol Lee (1 shared paper)Minsu Park (1 shared paper)Eun‐Young Kim (1 shared paper)Dylan Fong (1 shared paper)Robin E. Bachelder (1 shared paper)Kevin Richard Smith (1 shared paper)Sahar Alkhairy (1 shared paper)
- Journals
- Nature Methods (1 paper)Briefings in Bioinformatics (1 paper)Protein Science (1 paper)PLoS Computational Biology (1 paper)Cancer Discovery (1 paper)
- Partner nations
- South KoreaUnited StatesUnited Kingdom
In The Last Decade
Ingoo Lee
8 papers receiving 795 citations
Ingoo Lee's Hit Papers
Peers
Comparison fields: 5 of 75
- Computational Theory and Mathematics 569
- Molecular Biology 520
- Microbiology 43
- Health Informatics 10
- Biophysics 24
Countries citing papers authored by Ingoo Lee
This map shows the geographic impact of Ingoo 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 Ingoo Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ingoo Lee more than expected).
Fields of papers citing papers by Ingoo Lee
This network shows the impact of papers produced by Ingoo 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 Ingoo Lee. The network helps show where Ingoo Lee may publish in the future.
Co-authors
The 21 scholars most cited alongside Ingoo 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 | DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences Hit paper breakdown → | 2019 | 475 |
| 2 | 2020 | 88 | |
| 3 | 2022 | 83 | |
| 4 | 2018 | 50 | |
| 5 | 2022 | 48 | |
| 6 | 2024 | 33 | |
| 7 | 2022 | 22 | |
| 8 | 2022 | 13 | |
| 9 | 2026 | 0 |
About Ingoo Lee
Ingoo Lee is a scholar working on Computational Theory and Mathematics, Molecular Biology, Microbiology, Materials Chemistry and Cardiology and Cardiovascular Medicine, having authored 9 papers that have together received 812 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (5 papers), Machine Learning in Materials Science (3 papers), Machine Learning in Bioinformatics (2 papers), Protein Structure and Dynamics (2 papers), Biochemical and Structural Characterization (1 paper), Biomedical Text Mining and Ontologies (1 paper), Gene expression and cancer classification (1 paper) and Antimicrobial Peptides and Activities (1 paper). The work is most often cited by research in Computational Theory and Mathematics (569 citations), Molecular Biology (520 citations), Microbiology (43 citations), Health Informatics (10 citations) and Biophysics (24 citations). Ingoo Lee has collaborated with scholars based in South Korea, United States and United Kingdom. Frequent co-authors include Hojung Nam, Hansol Lee, Minsu Park, Eun‐Young Kim, Minsu Park, Dylan Fong, Robin E. Bachelder, Kevin Richard Smith, Sahar Alkhairy and Rudolf Pillich. Their work appears in journals such as Nature Methods, Briefings in Bioinformatics, Protein Science, PLoS Computational Biology and Cancer Discovery.
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.