Guiying Yan

6.5k total citations · 3 hit papers
83 papers, 5.1k citations indexed

About

Guiying Yan is a scholar working on Molecular Biology, Electrical and Electronic Engineering and Computational Theory and Mathematics. According to data from OpenAlex, Guiying Yan has authored 83 papers receiving a total of 5.1k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Molecular Biology, 25 papers in Electrical and Electronic Engineering and 21 papers in Computational Theory and Mathematics. Recurrent topics in Guiying Yan's work include Cancer-related molecular mechanisms research (21 papers), MicroRNA in disease regulation (15 papers) and Bioinformatics and Genomic Networks (12 papers). Guiying Yan is often cited by papers focused on Cancer-related molecular mechanisms research (21 papers), MicroRNA in disease regulation (15 papers) and Bioinformatics and Genomic Networks (12 papers). Guiying Yan collaborates with scholars based in China, United Kingdom and United States. Guiying Yan's co-authors include Xing Chen, Xing Chen, Ming-Xi Liu, Zhu‐Hong You, Qinghua Cui, Geng Chen, Yu‐An Huang, Zhi-An Huang, Mingxi Liu and Qipeng Zhang and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLoS ONE.

In The Last Decade

Guiying Yan

77 papers receiving 5.1k citations

Hit Papers

LncRNADisease: a database for long-non-coding RNA-associa... 2012 2026 2016 2021 2012 2013 2012 200 400 600

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Guiying Yan China 27 4.2k 3.2k 748 191 187 83 5.1k
Ping Xuan China 29 1.6k 0.4× 1.0k 0.3× 464 0.6× 456 2.4× 92 0.5× 122 2.6k
Jiawei Luo China 30 2.3k 0.6× 1.3k 0.4× 495 0.7× 315 1.6× 68 0.4× 154 3.1k
Yijie Ding China 37 3.4k 0.8× 434 0.1× 1.2k 1.5× 337 1.8× 58 0.3× 181 4.3k
Ali Masoudi‐Nejad Iran 34 2.4k 0.6× 361 0.1× 961 1.3× 330 1.7× 29 0.2× 165 3.7k
Yu‐An Huang China 36 2.6k 0.6× 1.4k 0.5× 603 0.8× 191 1.0× 19 0.1× 134 3.6k
Jialiang Yang China 36 2.3k 0.6× 849 0.3× 522 0.7× 448 2.3× 60 0.3× 182 4.1k
Liang Yu China 30 1.5k 0.4× 266 0.1× 433 0.6× 117 0.6× 170 0.9× 103 2.3k
Xiaogang Wu United States 22 874 0.2× 284 0.1× 216 0.3× 74 0.4× 96 0.5× 98 1.7k
Alfredo Ferro Italy 30 1.4k 0.3× 784 0.2× 350 0.5× 395 2.1× 11 0.1× 109 2.3k
Danfeng Sun China 19 1.9k 0.5× 664 0.2× 129 0.2× 156 0.8× 113 0.6× 83 3.3k

Countries citing papers authored by Guiying Yan

Since Specialization
Citations

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

Fields of papers citing papers by Guiying Yan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Guiying Yan

This figure shows the co-authorship network connecting the top 25 collaborators of Guiying Yan. A scholar is included among the top collaborators of Guiying Yan 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 Guiying Yan. Guiying Yan 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.
Yan, Guiying, et al.. (2025). Performance Analysis of Perturbation-Enhanced SC Decoders. IEEE Communications Letters. 29(3). 507–511.
2.
Tian, Lixia, et al.. (2025). Predicting drug combination side effects based on a metapath-based heterogeneous graph neural network. BMC Bioinformatics. 26(1). 16–16. 1 indexed citations
3.
Li, Bei, Haiqiang Jin, Guiying Yan, et al.. (2024). Mental states in caregivers toward people with Alzheimer’s disease at different stages. Frontiers in Neurology. 14. 1327487–1327487. 1 indexed citations
4.
Liu, Zhicheng, Yuan Li, Huazi Zhang, et al.. (2024). Second-Order Identification Capacity of AWGN Channels. 309–314.
5.
Wang, Qi, et al.. (2024). A novel graph neural network method for Alzheimer’s disease classification. Computers in Biology and Medicine. 180. 108869–108869. 10 indexed citations
6.
Yan, Guiying, et al.. (2023). Rainbow Turán numbers of matchings and forests of hyperstars in uniform hypergraphs. Discrete Mathematics. 346(9). 113481–113481. 1 indexed citations
7.
Li, Tong, et al.. (2022). Anti‐Ramsey number of expansions of paths and cycles in uniform hypergraphs. Journal of Graph Theory. 101(4). 668–685. 4 indexed citations
8.
Wang, Qi & Guiying Yan. (2019). IDLDA: An Improved Diffusion Model for Predicting LncRNA–Disease Associations. Frontiers in Genetics. 10. 1259–1259. 5 indexed citations
9.
Chen, Xing, Zhichao Jiang, Di Xie, et al.. (2017). A novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction. Molecular BioSystems. 13(6). 1202–1212. 44 indexed citations
10.
You, Zhu‐Hong, Zhi-An Huang, Zexuan Zhu, et al.. (2017). PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLoS Computational Biology. 13(3). e1005455–e1005455. 325 indexed citations
11.
Wang, Fan, Zhi-An Huang, Xing Chen, et al.. (2017). LRLSHMDA: Laplacian Regularized Least Squares for Human Microbe–Disease Association prediction. Scientific Reports. 7(1). 7601–7601. 106 indexed citations
12.
An, Jiyong, et al.. (2016). Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information. Molecular BioSystems. 12(12). 3702–3710. 9 indexed citations
13.
Chen, Xing, Biao Ren, Ming Chen, et al.. (2016). NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning. PLoS Computational Biology. 12(7). e1004975–e1004975. 236 indexed citations
14.
Wang, Guanghui, et al.. (2015). Orthogonal matchings revisited. Discrete Mathematics. 338(11). 2080–2088. 2 indexed citations
15.
Liu, Ming-Xi, Xing Chen, Geng Chen, Qinghua Cui, & Guiying Yan. (2014). A Computational Framework to Infer Human Disease-Associated Long Noncoding RNAs. PLoS ONE. 9(1). e84408–e84408. 110 indexed citations
16.
Chen, Xing & Guiying Yan. (2014). Semi-supervised learning for potential human microRNA-disease associations inference. Scientific Reports. 4(1). 5501–5501. 325 indexed citations
17.
Chen, Xing, Ming-Xi Liu, & Guiying Yan. (2012). Drug–target interaction prediction by random walk on the heterogeneous network. Molecular BioSystems. 8(7). 1970–1978. 417 indexed citations breakdown →
18.
Chen, Xing, Ming-Xi Liu, & Guiying Yan. (2012). RWRMDA: predicting novel human microRNA–disease associations. Molecular BioSystems. 8(10). 2792–2798. 366 indexed citations
19.
Chen, Geng, Ziyun Wang, Dongqing Wang, et al.. (2012). LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic Acids Research. 41(D1). D983–D986. 748 indexed citations breakdown →
20.
Lam, Peter Che Bor, et al.. (2001). Linear vertex arboricity, independence number and clique cover number. Ars Combinatoria. 58. 121–128. 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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