Thuc Duy Le

3.7k total citations · 1 hit paper
91 papers, 2.3k citations indexed

About

Thuc Duy Le is a scholar working on Molecular Biology, Cancer Research and Artificial Intelligence. According to data from OpenAlex, Thuc Duy Le has authored 91 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 48 papers in Molecular Biology, 32 papers in Cancer Research and 25 papers in Artificial Intelligence. Recurrent topics in Thuc Duy Le's work include MicroRNA in disease regulation (25 papers), Cancer-related molecular mechanisms research (24 papers) and RNA Research and Splicing (22 papers). Thuc Duy Le is often cited by papers focused on MicroRNA in disease regulation (25 papers), Cancer-related molecular mechanisms research (24 papers) and RNA Research and Splicing (22 papers). Thuc Duy Le collaborates with scholars based in Australia, China and United States. Thuc Duy Le's 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 and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Thuc Duy Le

84 papers receiving 2.2k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

GraphDTA: predicting drug–target binding affinity with graph neural networks

2020 629 citations Thin Nguyen, Hang Le et al. Bioinformatics profile →

Peers

Thuc Duy Le

MB

CR

CTM

AI

MC

Kwong‐Sak Leung Hong Kong

Alexander Aliper Russia

Paolo Magni Italy

Kevin Y. Yip Hong Kong

Juan Liu China

Jiajie Peng China

Zeynep H. Gümüş United States

Ola Spjuth Sweden

Ina Koch Germany

Kwong‐Sak Leung Hong Kong

Thuc Duy Le

910 ×0.6

MB

108 ×0.2

CR

735 ×1.2

CTM

519 ×1.4

AI

201 ×1.0

MC

Citations per year, relative to Thuc Duy Le Thuc Duy Le (= 1×) peers Kwong‐Sak Leung

Countries citing papers authored by Thuc Duy Le

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 of co-authors of Thuc Duy Le

This figure shows the co-authorship network connecting the top 25 collaborators of Thuc Duy Le. A scholar is included among the top collaborators of Thuc Duy Le based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Thuc Duy Le. Thuc Duy Le is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Liu, Lin, et al.. (2025). Dependency-based anomaly detection: A general framework and comprehensive evaluation. Expert Systems with Applications. 297. 129249–129249.

2.

Li, Xiaomei, Junpeng Zhang, Marnie Winter, et al.. (2025). Omics-based computational approaches for biomarker identification, prediction, and treatment of Long COVID. Critical Reviews in Clinical Laboratory Sciences. 1–27.

3.

Cheng, Debo, et al.. (2024). Instrumental Variable Estimation for Causal Inference in Longitudinal Data with Time-Dependent Latent Confounders. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 11480–11488. 2 indexed citations

4.

Huy, Nguyễn Văn, et al.. (2024). Crosstalk between genomic variants and DNA methylation in FLT3 mutant acute myeloid leukemia. Briefings in Functional Genomics. 24.

5.

Zhang, Junpeng, et al.. (2024). Scanning sample-specific miRNA regulation from bulk and single-cell RNA-sequencing data. BMC Biology. 22(1). 218–218. 3 indexed citations

6.

Cheng, Debo, et al.. (2024). Disentangled Representation Learning for Causal Inference With Instruments. IEEE Transactions on Neural Networks and Learning Systems. 36(8). 14078–14091. 3 indexed citations

7.

Le, Thuc Duy, Khalid T. Rashid, Evan R. Abt, et al.. (2023). Generation of liver metastases in a mouse model using ultrasound-guided intravenous injection. STAR Protocols. 4(2). 102163–102163. 2 indexed citations

8.

Cheng, Debo, Jiuyong Li, Lin Liu, et al.. (2022). Sufficient dimension reduction for average causal effect estimation. Data Mining and Knowledge Discovery. 36(3). 1174–1196. 6 indexed citations

9.

Le, Thuc Duy, et al.. (2022). Decision Support for Disability Employment using Counterfactual Survival Analysis. 2022 IEEE International Conference on Big Data (Big Data). 30. 2103–2112.

10.

Pham, Vu, Lin Liu, Cameron P. Bracken, et al.. (2021). pDriver : a novel method for unravelling personalized coding and miRNA cancer drivers. Bioinformatics. 37(19). 3285–3292. 9 indexed citations

11.

Pham, Vu, et al.. (2020). A pseudotemporal causality approach to identifying miRNA–mRNA interactions during biological processes. Bioinformatics. 37(6). 807–814. 2 indexed citations

12.

Pham, Vu, Lin Liu, Cameron P. Bracken, et al.. (2020). DriverGroup : a novel method for identifying driver gene groups. Bioinformatics. 36(Supplement_2). i583–i591. 5 indexed citations

13.

Nguyen, Thin, Hang Le, Thomas P. Quinn, et al.. (2020). GraphDTA: predicting drug–target binding affinity with graph neural networks. Bioinformatics. 37(8). 1140–1147. 629 indexed citations breakdown →

14.

Pham, Vu, Xiaomei Li, Buu Truong, et al.. (2020). The winning methods for predicting cellular position in the DREAM single-cell transcriptomics challenge. Briefings in Bioinformatics. 22(3). 11 indexed citations

15.

Zhang, Junpeng, Taosheng Xu, Lin Liu, et al.. (2020). LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer. PLoS Computational Biology. 16(4). e1007851–e1007851. 21 indexed citations

16.

Pillman, Katherine A., Andrew G. Bert, John Toubia, et al.. (2019). Extensive transcriptional responses are co-ordinated by microRNAs as revealed by Exon–Intron Split Analysis (EISA). Nucleic Acids Research. 47(16). 8606–8619. 7 indexed citations

17.

Le, Thuc Duy, et al.. (2018). Preface: The 2018 ACM SIGKDD Workshop on Causal Discovery.. Knowledge Discovery and Data Mining. 1–2. 1 indexed citations

18.

Le, Thuc Duy, Junpeng Zhang, Lin Liu, & Jiuyong Li. (2016). Computational methods for identifying miRNA sponge interactions. Briefings in Bioinformatics. 18(4). bbw042–bbw042. 81 indexed citations

19.

Zhang, Junpeng, Thuc Duy Le, Lin Liu, Jianfeng He, & Jiuyong Li. (2015). Identifying miRNA synergistic regulatory networks in heterogeneous human data via network motifs. Molecular BioSystems. 12(2). 454–463. 6 indexed citations

20.

Le, Thuc Duy, Lin Liu, Bing Liu, et al.. (2013). Inferring microRNA and transcription factor regulatory networks in heterogeneous data. BMC Bioinformatics. 14(1). 92–92. 31 indexed citations

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.

Explore authors with similar magnitude of impact