Kuo Yang

2.3k total citations · 1 hit paper
95 papers, 1.6k citations indexed

About

Kuo Yang is a scholar working on Molecular Biology, Computational Theory and Mathematics and Plant Science. According to data from OpenAlex, Kuo Yang has authored 95 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Molecular Biology, 18 papers in Computational Theory and Mathematics and 15 papers in Plant Science. Recurrent topics in Kuo Yang's work include Bioinformatics and Genomic Networks (23 papers), Computational Drug Discovery Methods (17 papers) and Metabolomics and Mass Spectrometry Studies (10 papers). Kuo Yang is often cited by papers focused on Bioinformatics and Genomic Networks (23 papers), Computational Drug Discovery Methods (17 papers) and Metabolomics and Mass Spectrometry Studies (10 papers). Kuo Yang collaborates with scholars based in China, United States and United Kingdom. Kuo Yang's co-authors include Xuezhong Zhou, Jianxin Chen, Chun‐Xiang You, Yi Zhao, Feilong Zhang, Liang Sun, Shuangsang Fang, Hui Li, Kuo Gao and Wei Wang and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and PLANT PHYSIOLOGY.

In The Last Decade

Kuo Yang

85 papers receiving 1.5k citations

Hit Papers

SymMap: an integrative database of traditional Chinese me... 2018 2026 2020 2023 2018 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kuo Yang China 20 866 314 298 222 170 95 1.6k
Zijing Liu China 22 465 0.5× 99 0.3× 216 0.7× 81 0.4× 89 0.5× 54 1.5k
Leihong Wu United States 18 477 0.6× 102 0.3× 87 0.3× 288 1.3× 224 1.3× 64 1.1k
Yühong Huang China 27 1.2k 1.4× 468 1.5× 155 0.5× 42 0.2× 436 2.6× 155 2.7k
Kailin Yang China 26 757 0.9× 278 0.9× 247 0.8× 71 0.3× 204 1.2× 103 2.1k
Zhixing Cao China 27 858 1.0× 76 0.2× 62 0.2× 288 1.3× 117 0.7× 77 2.0k
Muhammad Majid Pakistan 20 283 0.3× 122 0.4× 316 1.1× 41 0.2× 204 1.2× 58 1.5k
Jianshe Ma China 21 609 0.7× 143 0.5× 163 0.5× 36 0.2× 350 2.1× 102 1.3k
Peng Lu China 18 506 0.6× 120 0.4× 54 0.2× 116 0.5× 73 0.4× 61 983
Ran Duan China 20 381 0.4× 172 0.5× 56 0.2× 43 0.2× 89 0.5× 67 1.2k
Praveen Thaggikuppe Krishnamurthy India 23 516 0.6× 65 0.2× 136 0.5× 73 0.3× 71 0.4× 136 2.1k

Countries citing papers authored by Kuo Yang

Since Specialization
Citations

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

Fields of papers citing papers by Kuo Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kuo Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Kuo Yang. A scholar is included among the top collaborators of Kuo Yang 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 Kuo Yang. Kuo Yang 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.
Zhu, Kai, Tingyu Wang, Lei Wu, et al.. (2025). High-precision phenotyping of breast cancer exosomes based on washable magnetic microarrays and super-resolution tricolor fluorescence co-localization. Biosensors and Bioelectronics. 276. 117253–117253. 1 indexed citations
2.
Wang, Da‐Ru, Chunling Zhang, Xun Wang, et al.. (2025). MicroRNA156SPL13B Module Induces Parthenocarpy Through the Gibberellin Pathway. Plant Biotechnology Journal. 23(12). 5536–5549.
3.
Meng, Yuchen, et al.. (2024). Comparative Analysis of TPR Gene Family in Cucurbitaceae and Expression Profiling under Abiotic Stress in Cucumis melo L.. Horticulturae. 10(1). 83–83. 1 indexed citations
4.
Wang, Xinyan, Kuo Yang, Ting Jia, et al.. (2024). KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition. Briefings in Bioinformatics. 25(3). 6 indexed citations
7.
Liu, Menglin, et al.. (2024). New perspectives on microbiome-dependent gut-brain pathways for the treatment of depression with gastrointestinal symptoms: from bench to bedside. Journal of Zhejiang University SCIENCE B. 26(1). 1–25. 2 indexed citations
8.
Cai, Zhonglin, Jidong Xu, Kuo Yang, et al.. (2024). ELAVL1 regulates PD-L1 mRNA stability to disrupt the infiltration of CD4-positive T cells in prostate cancer. Neoplasia. 57. 101049–101049. 6 indexed citations
9.
Yang, Kuo, Hailong Sun, Qian Zhang, et al.. (2023). SympGAN: A systematic knowledge integration system for symptom–gene associations network. Knowledge-Based Systems. 276. 110752–110752. 6 indexed citations
10.
Wang, Da‐Ru, Chunling Zhang, Xun Wang, et al.. (2022). The Apple Lipoxygenase MdLOX3 Regulates Salt Tolerance and ABA Sensitivity. Horticulturae. 8(7). 651–651. 7 indexed citations
11.
Yang, Kuo, Zhenhong Liu, Xinxing Lai, et al.. (2022). DrugAI: a multi-view deep learning model for predicting drug–target activating/inhibiting mechanisms. Briefings in Bioinformatics. 24(1). 32 indexed citations
12.
Dong, Xin Luna, Yi Zheng, Zixin Shu, et al.. (2022). TCMPR: TCM Prescription Recommendation Based on Subnetwork Term Mapping and Deep Learning. BioMed Research International. 2022(1). 4845726–4845726. 25 indexed citations
13.
Chen, Xiaoxiao, Min Hou, Kuo Yang, et al.. (2022). The TCM Preparation Feilike Mixture for the Treatment of Pneumonia: Network Analysis, Pharmacological Assessment and Silico Simulation. Frontiers in Pharmacology. 13. 794405–794405. 9 indexed citations
14.
Yang, Kuo, Jian‐Ping An, Chong-Yang Li, et al.. (2021). The apple C2H2-type zinc finger transcription factor MdZAT10 positively regulates JA-induced leaf senescence by interacting with MdBT2. Horticulture Research. 8(1). 159–159. 37 indexed citations
15.
Yang, Kuo, et al.. (2021). Efficacy and Safety of TCMI in Patients With Combined Coronary Heart Disease and Heart Failure: A Systematic Review and Network Meta-Analysis. Frontiers in Pharmacology. 12. 741261–741261. 7 indexed citations
16.
Shu, Zixin, Kuo Yang, Jingjing Wang, et al.. (2020). Topological Analysis of the Language Networks of Ancient Traditional Chinese Medicine Books. Evidence-based Complementary and Alternative Medicine. 2020(1). 8810016–8810016. 2 indexed citations
17.
Yang, Kuo, Zixin Shu, Jingjing Wang, et al.. (2020). Integrated network analysis of symptom clusters across disease conditions. Journal of Biomedical Informatics. 107. 103482–103482. 13 indexed citations
18.
Wang, Gang, et al.. (2019). MiR-451 suppresses the growth, migration, and invasion of prostate cancer cells by targeting macrophage migration inhibitory factor. Translational Cancer Research. 8(2). 647–654. 1 indexed citations
19.
Yang, Kuo, Runshun Zhang, Liyun He, et al.. (2017). Multistage analysis method for detection of effective herb prescription from clinical data. Frontiers of Medicine. 12(2). 206–217. 13 indexed citations
20.
Yang, Kuo, et al.. (2016). Similarity-based algorithms for Disease Terminology Mapping. 2 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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