Yangkun Cao

418 total citations
13 papers, 272 citations indexed

About

Yangkun Cao is a scholar working on Molecular Biology, Cancer Research and Computational Theory and Mathematics. According to data from OpenAlex, Yangkun Cao has authored 13 papers receiving a total of 272 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 7 papers in Cancer Research and 4 papers in Computational Theory and Mathematics. Recurrent topics in Yangkun Cao's work include Cancer-related molecular mechanisms research (6 papers), Bioinformatics and Genomic Networks (5 papers) and RNA modifications and cancer (4 papers). Yangkun Cao is often cited by papers focused on Cancer-related molecular mechanisms research (6 papers), Bioinformatics and Genomic Networks (5 papers) and RNA modifications and cancer (4 papers). Yangkun Cao collaborates with scholars based in China, United Kingdom and United States. Yangkun Cao's co-authors include Ping Xuan, Tiangang Zhang, Xiao Wang, Nan Sheng, Zhaogong Zhang, Rui Kong, Lan Huang, Jian Liu, Jinmao Wei and Yan Wang and has published in prestigious journals such as Bioinformatics, International Journal of Molecular Sciences and Briefings in Bioinformatics.

In The Last Decade

Yangkun Cao

12 papers receiving 267 citations

Peers

Yangkun Cao
Ruijiang Li China
Guofei Ren China
Hossein Sharifi-Noghabi Canada
Qijin Yin China
Song-Yao Zhang China
Tingzhong Tian China
Sanath Kumar Ramesh United States
Kyuri Jo South Korea
Ruijiang Li China
Yangkun Cao
Citations per year, relative to Yangkun Cao Yangkun Cao (= 1×) peers Ruijiang Li

Countries citing papers authored by Yangkun Cao

Since Specialization
Citations

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

Fields of papers citing papers by Yangkun Cao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yangkun Cao

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

All Works

13 of 13 papers shown
1.
Cao, Yangkun, et al.. (2024). Elucidation of Factors Affecting the Age-Dependent Cancer Occurrence Rates. International Journal of Molecular Sciences. 26(1). 275–275. 4 indexed citations
2.
Feng, Ke, et al.. (2023). Identification of Cancer Driver Genes by Integrating Multiomics Data with Graph Neural Networks. Metabolites. 13(3). 339–339. 9 indexed citations
3.
Cao, Yangkun, et al.. (2023). X-LDA: An interpretable and knowledge-informed heterogeneous graph learning framework for LncRNA-disease association prediction. Computers in Biology and Medicine. 167. 107634–107634. 2 indexed citations
4.
Guan, Renchu, et al.. (2023). KISL: knowledge-injected semi-supervised learning for biological co-expression network modules. Frontiers in Genetics. 14. 1151962–1151962.
5.
Sheng, Nan, Yan Wang, Lan Huang, et al.. (2023). Multi-task prediction-based graph contrastive learning for inferring the relationship among lncRNAs, miRNAs and diseases. Briefings in Bioinformatics. 24(5). 29 indexed citations
6.
Sheng, Nan, et al.. (2023). A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20(5). 2810–2826. 12 indexed citations
7.
Cao, Yangkun, et al.. (2022). Molecular Subtyping of Cancer Based on Robust Graph Neural Network and Multi-Omics Data Integration. Frontiers in Genetics. 13. 884028–884028. 15 indexed citations
8.
Wang, Yan, et al.. (2022). Predicting miRNA-disease associations based on multi-view information fusion. Frontiers in Genetics. 13. 979815–979815. 7 indexed citations
9.
Sheng, Nan, Lan Huang, Jing Zhao, et al.. (2022). Multi-channel graph attention autoencoders for disease-related lncRNAs prediction. Briefings in Bioinformatics. 23(2). 25 indexed citations
10.
Cui, Hui, et al.. (2022). Learning multi-scale heterogenous network topologies and various pairwise attributes for drug–disease association prediction. Briefings in Bioinformatics. 23(2). 16 indexed citations
11.
Cao, Yangkun, et al.. (2021). Autoencoder-based drug–target interaction prediction by preserving the consistency of chemical properties and functions of drugs. Bioinformatics. 37(20). 3618–3625. 31 indexed citations
12.
Xuan, Ping, Yangkun Cao, Tiangang Zhang, Rui Kong, & Zhaogong Zhang. (2019). Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes. Frontiers in Genetics. 10. 416–416. 57 indexed citations
13.
Xuan, Ping, et al.. (2019). Drug repositioning through integration of prior knowledge and projections of drugs and diseases. Bioinformatics. 35(20). 4108–4119. 65 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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2026