Chang-Qing Yu

1.1k total citations
70 papers, 767 citations indexed

About

Chang-Qing Yu is a scholar working on Molecular Biology, Cancer Research and Computational Theory and Mathematics. According to data from OpenAlex, Chang-Qing Yu has authored 70 papers receiving a total of 767 indexed citations (citations by other indexed papers that have themselves been cited), including 51 papers in Molecular Biology, 27 papers in Cancer Research and 17 papers in Computational Theory and Mathematics. Recurrent topics in Chang-Qing Yu's work include Cancer-related molecular mechanisms research (26 papers), MicroRNA in disease regulation (23 papers) and Circular RNAs in diseases (20 papers). Chang-Qing Yu is often cited by papers focused on Cancer-related molecular mechanisms research (26 papers), MicroRNA in disease regulation (23 papers) and Circular RNAs in diseases (20 papers). Chang-Qing Yu collaborates with scholars based in China, United Kingdom and United States. Chang-Qing Yu's co-authors include Zhu‐Hong You, Zhong-Hao Ren, Jie Pan, Wenzhun Huang, Xinfei Wang, Liping Li, Lei Wang, Shanwen Zhang, Yu‐An Huang and Liping Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, International Journal of Molecular Sciences and IEEE Access.

In The Last Decade

Chang-Qing Yu

65 papers receiving 755 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chang-Qing Yu China 17 529 313 165 60 58 70 767
Yunxiang Li China 13 142 0.3× 56 0.2× 90 0.5× 34 0.6× 49 0.8× 54 476
Wenzheng Bao China 18 795 1.5× 148 0.5× 79 0.5× 13 0.2× 32 0.6× 59 1.1k
Béatrice Duval France 12 297 0.6× 68 0.2× 102 0.6× 19 0.3× 16 0.3× 20 540
Zhao Da China 12 264 0.5× 90 0.3× 19 0.1× 51 0.8× 243 4.2× 25 764
Tonghai Jiang China 11 476 0.9× 76 0.2× 150 0.9× 13 0.2× 8 0.1× 36 705
Guobo Xie China 14 250 0.5× 233 0.7× 52 0.3× 38 0.6× 5 0.1× 55 494
Yanbin Wang China 14 359 0.7× 47 0.2× 140 0.8× 13 0.2× 5 0.1× 41 522
Chanjuan Liu China 12 141 0.3× 34 0.1× 88 0.5× 101 1.7× 10 0.2× 41 409
Bingyi Wang China 11 378 0.7× 146 0.5× 55 0.3× 8 0.1× 21 0.4× 33 563

Countries citing papers authored by Chang-Qing Yu

Since Specialization
Citations

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

Fields of papers citing papers by Chang-Qing Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Chang-Qing 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 Chang-Qing Yu. The network helps show where Chang-Qing Yu may publish in the future.

Co-authorship network of co-authors of Chang-Qing Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Chang-Qing Yu. A scholar is included among the top collaborators of Chang-Qing 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 Chang-Qing Yu. Chang-Qing Yu 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.
2.
Liang, Shuai, et al.. (2025). Predicting circRNA–Disease Associations through Multisource Domain-Aware Embeddings and Feature Projection Networks. Journal of Chemical Information and Modeling. 65(3). 1666–1676. 9 indexed citations
3.
Liang, Shuai, Lei Wang, Zhu‐Hong You, & Chang-Qing Yu. (2025). A Multisource Transformer-Guided Graph Representation Learning Framework for circRNA-Disease Association Prediction. ACS Omega. 10(37). 43187–43200.
4.
Yu, Chang-Qing, et al.. (2024). Drug–drug interaction extraction based on multimodal feature fusion by Transformer and BiGRU. SHILAP Revista de lepidopterología. 4. 1 indexed citations
5.
Wang, Lei, Zhu‐Hong You, Chang-Qing Yu, et al.. (2024). Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA–miRNA associations. Briefings in Bioinformatics. 25(2). 20 indexed citations
6.
Wang, Lei, Zhu‐Hong You, Chang-Qing Yu, et al.. (2024). Biolinguistic graph fusion model for circRNA–miRNA association prediction. Briefings in Bioinformatics. 25(2). 10 indexed citations
7.
Zhang, Shanwen, et al.. (2023). Apple leaf disease recognition method based on Siamese dilated Inception network with less training samples. Computers and Electronics in Agriculture. 213. 108188–108188. 14 indexed citations
8.
Wang, Xinfei, Chang-Qing Yu, Zhu‐Hong You, et al.. (2023). KS-CMI: A circRNA-miRNA interaction prediction method based on the signed graph neural network and denoising autoencoder. iScience. 26(8). 107478–107478. 25 indexed citations
9.
Wang, Xinfei, Chang-Qing Yu, Zhu‐Hong You, et al.. (2023). A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks. Briefings in Bioinformatics. 24(3). 28 indexed citations
10.
Ren, Zhong-Hao, Quan Zou, Chang-Qing Yu, et al.. (2023). DeepMPF: deep learning framework for predicting drug–target interactions based on multi-modal representation with meta-path semantic analysis. Journal of Translational Medicine. 21(1). 48–48. 28 indexed citations
11.
Yu, Chang-Qing, Yan Qiao, Liping Li, et al.. (2022). MFIDMA: A Multiple Information Integration Model for the Prediction of Drug–miRNA Associations. Biology. 12(1). 41–41. 8 indexed citations
12.
13.
Pan, Jie, Shiwei Wang, Chang-Qing Yu, et al.. (2022). A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences. Biology. 11(5). 775–775. 3 indexed citations
14.
You, Zhu‐Hong, Lei Wang, Chang-Qing Yu, et al.. (2022). A novel circRNA-miRNA association prediction model based on structural deep neural network embedding. Briefings in Bioinformatics. 23(5). 41 indexed citations
15.
Pan, Jie, et al.. (2021). Prediction of Drug–Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method. Molecules. 26(17). 5359–5359. 3 indexed citations
16.
Pan, Jie, et al.. (2021). Sequence-Based Prediction of Plant Protein-Protein Interactions by Combining Discrete Sine Transformation With Rotation Forest. Evolutionary Bioinformatics. 17. 3243611219–3243611219. 6 indexed citations
17.
Huang, Wenzhun, et al.. (2021). An Ensemble Learning‐Based Method for Inferring Drug‐Target Interactions Combining Protein Sequences and Drug Fingerprints. BioMed Research International. 2021(1). 9933873–9933873. 6 indexed citations
18.
Pan, Jie, et al.. (2021). FWHT-RF: A Novel Computational Approach to Predict Plant Protein-Protein Interactions via an Ensemble Learning Method. Scientific Programming. 2021. 1–11. 7 indexed citations
19.
Huang, Wenzhun, et al.. (2021). A Sparse Feature Extraction Method with Elastic Net for Drug-Target Interaction Identification. Scientific Programming. 2021. 1–10. 3 indexed citations
20.
Yu, Chang-Qing, et al.. (2021). Research on the Application of Holographic Projection Technology in Display Design. 5(5). 106–108. 1 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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