Duolin Wang

2.0k total citations
43 papers, 1.2k citations indexed

About

Duolin Wang is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Duolin Wang has authored 43 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Molecular Biology, 4 papers in Artificial Intelligence and 4 papers in Computational Theory and Mathematics. Recurrent topics in Duolin Wang's work include Machine Learning in Bioinformatics (17 papers), Protein Structure and Dynamics (13 papers) and RNA and protein synthesis mechanisms (11 papers). Duolin Wang is often cited by papers focused on Machine Learning in Bioinformatics (17 papers), Protein Structure and Dynamics (13 papers) and RNA and protein synthesis mechanisms (11 papers). Duolin Wang collaborates with scholars based in United States, China and Denmark. Duolin Wang's co-authors include Dong Xu, Yanchun Liang, Yuexu Jiang, Trupti Joshi, Fei He, Shuai Zeng, Chunhui Xu, Wang‐Ren Qiu, Dongpeng Liu and Jingyi Li and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Bioinformatics.

In The Last Decade

Duolin Wang

38 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Duolin Wang United States 15 874 144 140 99 89 43 1.2k
Gabriella Rustici United Kingdom 16 1.5k 1.7× 143 1.0× 204 1.5× 135 1.4× 64 0.7× 26 1.9k
Mehdi Sadeghi Iran 17 970 1.1× 101 0.7× 59 0.4× 57 0.6× 137 1.5× 104 1.3k
Michael Yu United States 15 1.0k 1.2× 172 1.2× 79 0.6× 137 1.4× 121 1.4× 23 1.4k
Len Trigg New Zealand 5 735 0.8× 259 1.8× 64 0.5× 178 1.8× 100 1.1× 8 1.1k
Qi Dai China 23 1.2k 1.3× 67 0.5× 176 1.3× 132 1.3× 114 1.3× 98 1.4k
Zhenjun Hu United States 20 1.2k 1.4× 157 1.1× 68 0.5× 106 1.1× 231 2.6× 36 1.8k
Jung Eun Shim South Korea 14 887 1.0× 205 1.4× 133 0.9× 138 1.4× 76 0.9× 24 1.1k
Richard Holland United Kingdom 7 780 0.9× 177 1.2× 86 0.6× 61 0.6× 36 0.4× 11 1.0k
Kyungsook Han South Korea 25 1.5k 1.7× 94 0.7× 78 0.6× 144 1.5× 177 2.0× 100 1.8k

Countries citing papers authored by Duolin Wang

Since Specialization
Citations

This map shows the geographic impact of Duolin Wang'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 Duolin Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Duolin Wang more than expected).

Fields of papers citing papers by Duolin Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Duolin Wang. 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 Duolin Wang. The network helps show where Duolin Wang may publish in the future.

Co-authorship network of co-authors of Duolin Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Duolin Wang. A scholar is included among the top collaborators of Duolin Wang 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 Duolin Wang. Duolin Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Wang, Duolin, et al.. (2025). Kinase-substrate prediction using an autoregressive model. Computational and Structural Biotechnology Journal. 27. 1103–1111.
2.
Su, Li, Duolin Wang, & Dong Xu. (2025). Bridging peptide presentation and T cell recognition with multi-task learning. Nature Machine Intelligence. 7(2). 170–171.
3.
Zhang, Yichuan, et al.. (2025). Enhancing Structure-Aware Protein Language Models with Efficient Fine-Tuning for Various Protein Prediction Tasks. Methods in molecular biology. 2941. 31–58.
4.
Jiang, Yuexu, et al.. (2024). IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network. Journal of Advanced Research. 72. 319–331. 1 indexed citations
5.
Wang, Duolin, Shuai Zeng, Yuexu Jiang, et al.. (2024). S‐PLM: Structure‐Aware Protein Language Model via Contrastive Learning Between Sequence and Structure. Advanced Science. 12(5). e2404212–e2404212. 9 indexed citations
6.
Lin, Yu, Duolin Wang, Yuzhou Chang, et al.. (2024). A contrastive learning approach to integrate spatial transcriptomics and histological images. Computational and Structural Biotechnology Journal. 23. 1786–1795. 1 indexed citations
7.
Jiang, Yuexu, Lei Jiang, Duolin Wang, et al.. (2023). MULocDeep web service for protein localization prediction and visualization at subcellular and suborganellar levels. Nucleic Acids Research. 51(W1). W343–W349. 20 indexed citations
8.
Basu, Sushmita, Bi Zhao, Eshel Faraggi, et al.. (2023). DescribePROT in 2023: more, higher-quality and experimental annotations and improved data download options. Nucleic Acids Research. 52(D1). D426–D433. 7 indexed citations
9.
Wang, Duolin, et al.. (2023). Spatial-Aware Transformer (SAT): Enhancing Global Modeling in Transformer Segmentation for Remote Sensing Images. Remote Sensing. 15(14). 3607–3607. 3 indexed citations
10.
Wang, Duolin, Fei He, Yang Yu, & Dong Xu. (2023). Meta-learning for T cell receptor binding specificity and beyond. Nature Machine Intelligence. 5(4). 337–339. 7 indexed citations
11.
Guo, Zhiye, et al.. (2023). Diffusion models in bioinformatics and computational biology. Nature Reviews Bioengineering. 2(2). 136–154. 66 indexed citations
12.
Ma, Anjun, Xiaoying Wang, Jingxian Li, et al.. (2023). Single-cell biological network inference using a heterogeneous graph transformer. Nature Communications. 14(1). 964–964. 93 indexed citations
13.
Zeng, Shuai, et al.. (2021). G2PDeep: a web-based deep-learning framework for quantitative phenotype prediction and discovery of genomic markers. Nucleic Acids Research. 49(W1). W228–W236. 22 indexed citations
14.
Jiang, Yuexu, et al.. (2021). MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation. Computational and Structural Biotechnology Journal. 19. 4825–4839. 69 indexed citations
15.
Jiang, Yuexu, et al.. (2021). Computational methods for protein localization prediction. Computational and Structural Biotechnology Journal. 19. 5834–5844. 21 indexed citations
16.
Wang, Duolin, Dongpeng Liu, Fei He, et al.. (2020). MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization. Nucleic Acids Research. 48(W1). W140–W146. 183 indexed citations
17.
Khan, Saad M., Fei He, Duolin Wang, Yongbing Chen, & Dong Xu. (2020). MU-PseUDeep: A deep learning method for prediction of pseudouridine sites. Computational and Structural Biotechnology Journal. 18. 1877–1883. 11 indexed citations
18.
Jiang, Yuexu, Duolin Wang, Dong Xu, & Trupti Joshi. (2019). IMPRes-Pro: A high dimensional multiomics integration method for in silico hypothesis generation. Methods. 173. 16–23. 7 indexed citations
19.
Liu, Yang, Duolin Wang, Fei He, et al.. (2019). Phenotype Prediction and Genome-Wide Association Study Using Deep Convolutional Neural Network of Soybean. Frontiers in Genetics. 10. 1091–1091. 100 indexed citations
20.
Wang, Duolin, Juexin Wang, Yuexu Jiang, Yanchun Liang, & Dong Xu. (2016). BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-Expression Analysis. Journal of Molecular Biology. 429(3). 446–453. 6 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.

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