Hang Le
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
Papers in
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- Natural Language Processing Techniques 4
- Speech Recognition and Synthesis 3
- Sentiment Analysis and Opinion Mining 2
- Topic Modeling 2
- Speech and dialogue systems 1
- Text Readability and Simplification 1
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- Mental Health via Writing 2
- Co-authors
- Svetha Venkatesh (1 shared paper)Thomas P. Quinn (1 shared paper)Thuc Duy Le (1 shared paper)Tri Minh Nguyen (1 shared paper)Thin Nguyen (1 shared paper)Didier Schwab (4 shared papers)Laurent Besacier (3 shared papers)Juan Pino (2 shared papers)
- Journals
- Bioinformatics (1 paper)SN Computer Science (1 paper)HAL (Le Centre pour la Communication Scientifique Directe) (2 papers)arXiv (Cornell University) (1 paper)
In The Last Decade
Hang Le
5 papers receiving 753 citations
Hang Le's Hit Papers
Peers
Comparison fields: 5 of 58
- Computational Theory and Mathematics 579
- Molecular Biology 464
- Materials Chemistry 172
- Artificial Intelligence 104
- Pharmacology 45
Countries citing papers authored by Hang Le
This map shows the geographic impact of Hang Le'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 Hang Le with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hang Le more than expected).
Fields of papers citing papers by Hang Le
This network shows the impact of papers produced by Hang Le. 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 Hang Le. The network helps show where Hang Le may publish in the future.
Co-authors
The 23 scholars most cited alongside Hang Le, 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 | GraphDTA: predicting drug–target binding affinity with graph neural networks Hit paper breakdown → | 2020 | 690 |
| 2 | 2021 | 38 | |
| 3 | 2020 | 31 | |
| 4 | 2020 | 2 | |
| 5 | 2020 | 1 | |
| 6 | 2023 | 0 | |
| 7 | FlauBERT : des modèles de langue contextualisés pré-entraînés pour le français | 2020 | 0 |
About Hang Le
Hang Le is a scholar working on Artificial Intelligence, Social Psychology, Epidemiology, Signal Processing and Computational Theory and Mathematics, having authored 7 papers that have together received 762 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (4 papers), Speech Recognition and Synthesis (3 papers), Mental Health via Writing (2 papers), Sentiment Analysis and Opinion Mining (2 papers), Data-Driven Disease Surveillance (2 papers), Topic Modeling (2 papers), Speech and dialogue systems (1 paper) and Text Readability and Simplification (1 paper). The work is most often cited by research in Computational Theory and Mathematics (579 citations), Molecular Biology (464 citations), Materials Chemistry (172 citations), Artificial Intelligence (104 citations) and Pharmacology (45 citations). Hang Le has collaborated with scholars based in France, Vietnam and Australia. Frequent co-authors include Svetha Venkatesh, Thomas P. Quinn, Thuc Duy Le, Tri Minh Nguyen, Thin Nguyen, Didier Schwab, Laurent Besacier, Juan Pino, Changhan Wang and Jiatao Gu. Their work appears in journals such as Bioinformatics, SN Computer Science, HAL (Le Centre pour la Communication Scientifique Directe) and arXiv (Cornell University).
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.