Chun-Chun Wang

1.6k total citations
30 papers, 1.1k citations indexed

About

Chun-Chun Wang is a scholar working on Molecular Biology, Cancer Research and Computational Theory and Mathematics. According to data from OpenAlex, Chun-Chun Wang has authored 30 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Molecular Biology, 15 papers in Cancer Research and 7 papers in Computational Theory and Mathematics. Recurrent topics in Chun-Chun Wang's work include MicroRNA in disease regulation (15 papers), Cancer-related molecular mechanisms research (13 papers) and Circular RNAs in diseases (12 papers). Chun-Chun Wang is often cited by papers focused on MicroRNA in disease regulation (15 papers), Cancer-related molecular mechanisms research (13 papers) and Circular RNAs in diseases (12 papers). Chun-Chun Wang collaborates with scholars based in China and Taiwan. Chun-Chun Wang's co-authors include Xing Chen, Yan Zhao, Xing Chen, Jun Yin, Li Zhang, Tianhao Li, Qi Zhao, Xing Chen, Zhu‐Hong You and Jianqiang Li and has published in prestigious journals such as Genomics, Journal of Chemical Information and Modeling and Briefings in Bioinformatics.

In The Last Decade

Chun-Chun Wang

28 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Chun-Chun Wang China 21 953 569 341 91 38 30 1.1k
Na‐Na Guan China 9 785 0.8× 592 1.0× 199 0.6× 41 0.5× 26 0.7× 14 909
Ming-Xi Liu China 8 947 1.0× 542 1.0× 356 1.0× 66 0.7× 34 0.9× 13 1.1k
Minjie Mou China 14 664 0.7× 123 0.2× 284 0.8× 72 0.8× 43 1.1× 30 941
Shantao Li United States 12 397 0.4× 116 0.2× 83 0.2× 16 0.2× 29 0.8× 14 610
Samson Fong United States 7 510 0.5× 100 0.2× 230 0.7× 67 0.7× 109 2.9× 11 790
Shuting Jin China 14 507 0.5× 69 0.1× 357 1.0× 139 1.5× 88 2.3× 31 739
Junlin Xu China 17 516 0.5× 214 0.4× 187 0.5× 20 0.2× 77 2.0× 53 743
Zhaorong Li China 12 430 0.5× 87 0.2× 171 0.5× 39 0.4× 42 1.1× 17 603
Yajie Meng China 14 524 0.5× 124 0.2× 372 1.1× 45 0.5× 85 2.2× 36 717
Yaping Fang China 16 573 0.6× 91 0.2× 57 0.2× 29 0.3× 25 0.7× 37 792

Countries citing papers authored by Chun-Chun Wang

Since Specialization
Citations

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

Fields of papers citing papers by Chun-Chun Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chun-Chun Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Chun-Chun Wang. A scholar is included among the top collaborators of Chun-Chun 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 Chun-Chun Wang. Chun-Chun 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.
Liu, Shaopeng, et al.. (2025). A new approach for microbe-disease association prediction: incorporating representation learning of latent relationships. BMC Medical Informatics and Decision Making. 25(1). 270–270.
2.
Zhao, Yan, Chun-Chun Wang, Fei Chu, et al.. (2024). RFEM: A framework for essential microRNA identification in mice based on rotation forest and multiple feature fusion. Computers in Biology and Medicine. 171. 108177–108177. 9 indexed citations
3.
Lu, Xu, et al.. (2024). Deep Augmented Metric Learning Network for Prostate Cancer Classification in Ultrasound Images. IEEE Journal of Biomedical and Health Informatics. 29(3). 1849–1860. 1 indexed citations
4.
Zhang, Li, Chun-Chun Wang, Zhang Yon, & Xing Chen. (2023). GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores. Computers in Biology and Medicine. 166. 107512–107512. 29 indexed citations
5.
Wang, Chun-Chun, et al.. (2023). MCFF-MTDDI: multi-channel feature fusion for multi-typed drug–drug interaction prediction. Briefings in Bioinformatics. 24(4). 34 indexed citations
6.
Wang, Chun-Chun, Tianhao Li, Huang Li, & Xing Chen. (2022). Prediction of potential miRNA–disease associations based on stacked autoencoder. Briefings in Bioinformatics. 23(2). 52 indexed citations
7.
Zhang, Li, Chun-Chun Wang, & Xing Chen. (2022). Predicting drug–target binding affinity through molecule representation block based on multi-head attention and skip connection. Briefings in Bioinformatics. 23(6). 71 indexed citations
8.
Qu, Jia, et al.. (2021). Biased Random Walk With Restart on Multilayer Heterogeneous Networks for MiRNA–Disease Association Prediction. Frontiers in Genetics. 12. 720327–720327. 8 indexed citations
9.
Chen, Xing, Chi Zhou, Chun-Chun Wang, & Yan Zhao. (2021). Predicting potential small molecule–miRNA associations based on bounded nuclear norm regularization. Briefings in Bioinformatics. 22(6). 62 indexed citations
10.
Liu, Yanping, Chun-Chun Wang, & Shijie Li. (2021). A fractal Langmuir kinetic equation and its solution structure. Thermal Science. 25(2 Part B). 1351–1354. 16 indexed citations
11.
Wang, Chun-Chun, et al.. (2021). Identification of miRNA–disease associations via multiple information integration with Bayesian ranking. Briefings in Bioinformatics. 22(6). 21 indexed citations
12.
Wang, Chun-Chun, et al.. (2021). Ensemble of kernel ridge regression-based small molecule–miRNA association prediction in human disease. Briefings in Bioinformatics. 23(1). 36 indexed citations
13.
Wang, Chun-Chun, et al.. (2021). Circular RNAs and complex diseases: from experimental results to computational models. Briefings in Bioinformatics. 22(6). 130 indexed citations
14.
Guan, Na‐Na, et al.. (2019). Anticancer Drug Response Prediction in Cell Lines Using Weighted Graph Regularized Matrix Factorization. Molecular Therapy — Nucleic Acids. 17. 164–174. 65 indexed citations
15.
Chen, Xing, Shaoxin Li, Jun Yin, & Chun-Chun Wang. (2019). Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization. Genomics. 112(1). 809–819. 35 indexed citations
16.
Wang, Chun-Chun & Xing Chen. (2019). A Unified Framework for the Prediction of Small Molecule–MicroRNA Association Based on Cross-Layer Dependency Inference on Multilayered Networks. Journal of Chemical Information and Modeling. 59(12). 5281–5293. 25 indexed citations
17.
Yin, Jun, et al.. (2019). Prediction of Small Molecule–MicroRNA Associations by Sparse Learning and Heterogeneous Graph Inference. Molecular Pharmaceutics. 16(7). 3157–3166. 42 indexed citations
18.
Qu, Jia, Yan Zhao, Li Zhang, et al.. (2019). Computational Models for Self-Interacting Proteins Prediction. Protein and Peptide Letters. 27(5). 392–399. 1 indexed citations
19.
Chen, Xing, Chun-Chun Wang, Jun Yin, & Zhu‐Hong You. (2018). Novel Human miRNA-Disease Association Inference Based on Random Forest. Molecular Therapy — Nucleic Acids. 13. 568–579. 100 indexed citations
20.
Wang, Chun-Chun, et al.. (2010). Effects of biotic and abiotic factors on the oxygen content of green sea turtle nests during embryogenesis. Journal of Comparative Physiology B. 180(7). 1045–1055. 29 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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