Dong‐Jun Yu

6.0k total citations · 1 hit paper
153 papers, 4.5k citations indexed

About

Dong‐Jun Yu is a scholar working on Molecular Biology, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Dong‐Jun Yu has authored 153 papers receiving a total of 4.5k indexed citations (citations by other indexed papers that have themselves been cited), including 112 papers in Molecular Biology, 45 papers in Computational Theory and Mathematics and 21 papers in Artificial Intelligence. Recurrent topics in Dong‐Jun Yu's work include Machine Learning in Bioinformatics (85 papers), RNA and protein synthesis mechanisms (50 papers) and Protein Structure and Dynamics (40 papers). Dong‐Jun Yu is often cited by papers focused on Machine Learning in Bioinformatics (85 papers), RNA and protein synthesis mechanisms (50 papers) and Protein Structure and Dynamics (40 papers). Dong‐Jun Yu collaborates with scholars based in China, Australia and United States. Dong‐Jun Yu's co-authors include Jingyu Yang, Xibei Yang, Jun Hu, Hong‐Bin Shen, Yang Zhang, Muhammad Arif, Jiangning Song, Tsau Young Lin, Yan Li and Muhammad Kabir and has published in prestigious journals such as Bioinformatics, PLoS ONE and Analytical Biochemistry.

In The Last Decade

Dong‐Jun Yu

145 papers receiving 4.4k 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.

Combination of interval-valued fuzzy set and soft set

2009 368 citations Xibei Yang, Tsau Young Lin et al. Computers & Mathematics with Applications profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dong‐Jun Yu China	39	2.8k	1.5k	774	494	392	153	4.5k
Natalio Krasnogor United Kingdom	39	2.0k 0.7×	970 0.6×	1.8k 2.4×	141 0.3×	125 0.3×	163	4.8k
Hiroshi Mamitsuka Japan	35	2.7k 1.0×	1.0k 0.7×	1.1k 1.5×	41 0.1×	181 0.5×	180	4.3k
Yansen Su China	28	746 0.3×	906 0.6×	1.1k 1.4×	125 0.3×	290 0.7×	106	2.5k
Ka‐Chun Wong Hong Kong	36	1.6k 0.6×	1.1k 0.7×	1.5k 1.9×	122 0.2×	102 0.3×	220	4.1k
William B. Langdon United Kingdom	34	1.3k 0.5×	679 0.5×	3.1k 4.0×	162 0.3×	873 2.2×	157	5.3k
Maozu Guo China	29	2.1k 0.8×	593 0.4×	826 1.1×	153 0.3×	262 0.7×	214	3.7k
Jacek Błażewicz Poland	40	1.9k 0.7×	557 0.4×	682 0.9×	1.1k 2.2×	196 0.5×	289	7.4k
Shaoliang Peng China	28	1.5k 0.5×	374 0.2×	441 0.6×	107 0.2×	286 0.7×	190	3.1k
Linqiang Pan China	44	4.4k 1.6×	1.6k 1.1×	960 1.2×	153 0.3×	57 0.1×	244	5.9k
Miguel Rocha Portugal	29	1.9k 0.7×	298 0.2×	539 0.7×	104 0.2×	96 0.2×	179	3.5k

Countries citing papers authored by Dong‐Jun Yu

Since Specialization

Citations

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

Fields of papers citing papers by Dong‐Jun Yu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dong‐Jun Yu. 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 Dong‐Jun Yu. The network helps show where Dong‐Jun Yu may publish in the future.

Co-authorship network of co-authors of Dong‐Jun Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Dong‐Jun Yu. A scholar is included among the top collaborators of Dong‐Jun Yu 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 Dong‐Jun Yu. Dong‐Jun Yu 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

Liu, Yan, Wei Guo, Jia‐shun Wu, et al.. (2025). MUSIC-GCN: A Novel Multi-Tasking Pipeline for Analyzing Single-Cell Transcriptomic Data Using Residual Graph Convolution Network. PubMed. 22(2). 781–789.

Zhang, Yumeng, Zhikang Wang, Dene R. Littler, et al.. (2025). Self-iterative multiple-instance learning enables the prediction of CD4+ T cell immunogenic epitopes. Nature Machine Intelligence. 7(8). 1250–1265.

Cai, Cheng, Zhaohong Deng, Andong Li, et al.. (2025). CATransUnetLBP: Accurate Prediction of Protein-Ligand Binding Pockets Using a Hybrid Network. PubMed. 22(1). 355–367.

Zhang, Ziqi, Hailong Yang, Te Zhang, et al.. (2025). m2ST: dual multi-scale graph clustering for spatially resolved transcriptomics. Bioinformatics. 41(5).

Zhang, Ying, Dian Liu, Yiheng Zhu, et al.. (2024). BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-Methyladenosine Sites. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(6). 1803–1815. 3 indexed citations

Wu, Jia‐shun, Yan Liu, Yiheng Zhu, & Dong‐Jun Yu. (2024). Improving Antifreeze Proteins Prediction With Protein Language Models and Hybrid Feature Extraction Networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 21(6). 2349–2358. 1 indexed citations

Li, Shuo, et al.. (2024). GMFGRN: a matrix factorization and graph neural network approach for gene regulatory network inference. Briefings in Bioinformatics. 25(2). 15 indexed citations

Yang, Xibei, et al.. (2024). GPTrans: A Biological Language Model-Based Approach for Predicting Disease-Associated Mutations in G Protein-Coupled Receptors. Journal of Chemical Information and Modeling. 64(24). 9626–9642. 2 indexed citations

Ge, Fang, et al.. (2024). FCMSTrans: Accurate Prediction of Disease-Associated nsSNPs by Utilizing Multiscale Convolution and Deep Feature Combination within a Transformer Framework. Journal of Chemical Information and Modeling. 64(4). 1394–1406. 12 indexed citations

10.

Arif, Muhammad, et al.. (2024). iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids. ACS Omega. 9(2). 2874–2883. 13 indexed citations

11.

Zhang, Ming, et al.. (2024). MetalTrans: A Biological Language Model-Based Approach for Predicting Disease-Associated Mutations in Protein Metal-Binding Sites. Journal of Chemical Information and Modeling. 64(15). 6216–6229. 4 indexed citations

12.

Wang, Pingxin, et al.. (2023). Glee: A granularity filter for feature selection. Engineering Applications of Artificial Intelligence. 122. 106080–106080. 12 indexed citations

13.

Yan, He, Yan Liu, Yanmeng Li, et al.. (2023). Robust GEPSVM classifier: An efficient iterative optimization framework. Information Sciences. 657. 119986–119986. 2 indexed citations

14.

Zhang, Ying, Zhikang Wang, Yiwen Zhang, et al.. (2023). Interpretable prediction models for widespread m6A RNA modification across cell lines and tissues. Bioinformatics. 39(12). 11 indexed citations

15.

Pan, Xiaoyong, et al.. (2023). MLNGCF: circRNA–disease associations prediction with multilayer attention neural graph-based collaborative filtering. Bioinformatics. 39(8). 13 indexed citations

16.

Pan, Xiaoyong, Hong‐Bin Shen, Kup‐Sze Choi, et al.. (2022). MDGF-MCEC: a multi-view dual attention embedding model with cooperative ensemble learning for CircRNA-disease association prediction. Briefings in Bioinformatics. 23(5). 14 indexed citations

17.

Li, Yang, Chengxin Zhang, Eric W. Bell, et al.. (2021). Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks. PLoS Computational Biology. 17(3). e1008865–e1008865. 59 indexed citations

18.

Han, Ke, et al.. (2016). Protein–protein interaction sites prediction by ensembling SVM and sample-weighted random forests. Neurocomputing. 193. 201–212. 98 indexed citations

19.

Yang, Xibei, Tsau Young Lin, Jingyu Yang, Yan Li, & Dong‐Jun Yu. (2009). Combination of interval-valued fuzzy set and soft set. Computers & Mathematics with Applications. 58(3). 521–527. 368 indexed citations breakdown →

20.

Lee, Sang‐Kwon, et al.. (2005). Identification and Reduction of Gear Whine Noise of the Axle System in a Passenger Van. SAE technical papers on CD-ROM/SAE technical paper series. 1. 15 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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