Thuc Duy Le
- Cancer Research top 2%
- MicroRNA in disease regulation 25
- Cancer-related molecular mechanisms research 24
- Computational Theory and Mathematics top 0.5%
- Molecular Biology top 5%
- RNA Research and Splicing 22
- RNA modifications and cancer 12
- Gene expression and cancer classification 11
- Bioinformatics and Genomic Networks 11
- Statistics and Probability top 5%
- Advanced Causal Inference Techniques 11
- Artificial Intelligence top 5%
- Bayesian Modeling and Causal Inference 17
- Co-authors
- Jiuyong LiLin LiuThin NguyenSvetha VenkateshThomas P. QuinnTri Minh NguyenHang LeJunpeng Zhang
- Partner nations
- AustraliaChinaUnited States
In The Last Decade
Thuc Duy Le
84 papers receiving 2.2k citations
Hit Papers
Peers
Comparison fields: 5 of 152
- Cancer Research 671
- Computational Theory and Mathematics 624
- Molecular Biology 1.4k
- Statistics and Probability 110
- Artificial Intelligence 383
Countries citing papers authored by Thuc Duy Le
This map shows the geographic impact of Thuc Duy 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 Thuc Duy Le with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thuc Duy Le more than expected).
Fields of papers citing papers by Thuc Duy Le
This network shows the impact of papers produced by Thuc Duy 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 Thuc Duy Le. The network helps show where Thuc Duy Le may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Thuc Duy 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 | 2025 | 0 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 0 | |
| 5 | 2024 | 3 | |
| 6 | 2024 | 3 | |
| 7 | 2023 | 2 | |
| 8 | 2022 | 6 | |
| 9 | 2022 | 0 | |
| 10 | 2021 | 9 | |
| 11 | 2020 | 2 | |
| 12 | 2020 | 5 | |
| 13 | GraphDTA: predicting drug–target binding affinity with graph neural networksbreakdown → | 2020 | 629 |
| 14 | 2020 | 11 | |
| 15 | 2020 | 21 | |
| 16 | 2019 | 7 | |
| 17 | Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery. | 2018 | 1 |
| 18 | 2016 | 81 | |
| 19 | 2015 | 6 | |
| 20 | 2013 | 31 |
About Thuc Duy Le
Thuc Duy Le is a scholar working on Cancer Research, Statistics and Probability and Artificial Intelligence, having authored 91 papers that have together received 2.3k indexed citations. Recurring topics across this work include MicroRNA in disease regulation (25 papers), Cancer-related molecular mechanisms research (24 papers), RNA Research and Splicing (22 papers), Bayesian Modeling and Causal Inference (17 papers), RNA modifications and cancer (12 papers), Advanced Causal Inference Techniques (11 papers), Gene expression and cancer classification (11 papers) and Bioinformatics and Genomic Networks (11 papers). The work is most often cited by research in Cancer Research (671 citations), Computational Theory and Mathematics (624 citations) and Molecular Biology (1.4k citations). Thuc Duy Le has collaborated with scholars based in Australia, China and United States. Frequent co-authors include Jiuyong Li, Lin Liu, Thin Nguyen, Svetha Venkatesh, Thomas P. Quinn, Tri Minh Nguyen, Hang Le, Junpeng Zhang, Taosheng Xu and Bingyu Sun. Their work appears in journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.
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.