Jing Yang

6.9k total citations
165 papers, 4.0k citations indexed

About

Jing Yang is a scholar working on Molecular Biology, Plant Science and Ecology, Evolution, Behavior and Systematics. According to data from OpenAlex, Jing Yang has authored 165 papers receiving a total of 4.0k indexed citations (citations by other indexed papers that have themselves been cited), including 104 papers in Molecular Biology, 36 papers in Plant Science and 23 papers in Ecology, Evolution, Behavior and Systematics. Recurrent topics in Jing Yang's work include RNA and protein synthesis mechanisms (18 papers), Genomics and Phylogenetic Studies (16 papers) and Machine Learning in Bioinformatics (15 papers). Jing Yang is often cited by papers focused on RNA and protein synthesis mechanisms (18 papers), Genomics and Phylogenetic Studies (16 papers) and Machine Learning in Bioinformatics (15 papers). Jing Yang collaborates with scholars based in China, United States and Canada. Jing Yang's co-authors include Liu Hong, Jie Chen, K.-C. Chou, Xiang‐Qin Liu, K.-C. Chou, Hong‐Bin Shen, Jianghui Hou, Junbo Yang, Li D and Aparna Renigunta and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Journal of Biological Chemistry.

In The Last Decade

Jing Yang

154 papers receiving 4.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Jing Yang China 33 2.7k 641 374 366 265 165 4.0k
Ralph Schlapbach Switzerland 34 2.5k 0.9× 747 1.2× 123 0.3× 383 1.0× 159 0.6× 98 4.8k
Nan Li China 37 3.0k 1.1× 213 0.3× 470 1.3× 237 0.6× 87 0.3× 245 5.4k
Barbara Frewen United States 11 3.2k 1.2× 554 0.9× 220 0.6× 485 1.3× 30 0.1× 12 5.0k
Jiang Zhu China 35 4.1k 1.5× 1.1k 1.7× 185 0.5× 652 1.8× 42 0.2× 159 5.6k
Attila Csordás United Kingdom 17 4.0k 1.5× 565 0.9× 65 0.2× 496 1.4× 86 0.3× 24 6.3k
Dave Speijer Netherlands 35 2.6k 1.0× 636 1.0× 127 0.3× 344 0.9× 61 0.2× 105 4.6k
Joseph White United States 24 3.3k 1.2× 1.6k 2.5× 173 0.5× 730 2.0× 65 0.2× 43 5.4k
Mathias Walzer Germany 13 3.2k 1.2× 316 0.5× 60 0.2× 353 1.0× 70 0.3× 20 4.9k
José A. Dianes United Kingdom 8 3.2k 1.2× 494 0.8× 60 0.2× 391 1.1× 57 0.2× 11 5.0k
Johannes Griss Austria 25 4.1k 1.5× 520 0.8× 66 0.2× 438 1.2× 74 0.3× 61 6.5k

Countries citing papers authored by Jing Yang

Since Specialization
Citations

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

Fields of papers citing papers by Jing Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jing Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Jing Yang. A scholar is included among the top collaborators of Jing 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 Jing Yang. Jing 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.
2.
Wu, Xiao‐Lin, et al.. (2025). Identification of Critical Gut Metabolites Mediating the Protective Effects of Lacticaseibacillus paracasei on Myocardial Injury in Mice. Journal of Agricultural and Food Chemistry. 73(30). 18822–18840. 1 indexed citations
3.
4.
Li, Zhenqing, et al.. (2025). A comprehensive review of methodological and technological advancement in PCR during the last 15 years. Biotechnology Advances. 85. 108719–108719.
5.
Guo, Qian, Xiang Chen, Hui Gong, et al.. (2024). Effect of inoculated sludge concentration on start-up of anammox reactor: Nitrogen removal performance and metabolic pathways. Bioresource Technology. 418. 131883–131883. 1 indexed citations
6.
Yang, Jing, et al.. (2024). First international training course on conservation of Plant Species with Extremely Small Populations. Oryx. 58(1). 11–11. 1 indexed citations
7.
Shi, Zhiru, et al.. (2024). Deep Neural Network-Based Cloth Collision Detection Algorithm. Scientific Programming. 2024. 1–15.
8.
Zhou, Shuyan, Hui Gong, Xiang Chen, et al.. (2024). Start-up of a full-scale two-stage partial nitritation/anammox (PN/A) process treating reject water from high solid anaerobic sludge digestion (HSAD). Water Research X. 25. 100259–100259. 5 indexed citations
9.
Yang, Jing, Yumei Hua, Guanglong Liu, et al.. (2024). Characteristics of phosphorus transformation from vivianite mediated by sulphide. Journal of Environmental Sciences. 154. 52–62.
10.
Wang, Hongyang, Junhua Li, Hao Liu, et al.. (2023). Variability in morpho-biochemical, photosynthetic pigmentation, enzymatic and quality attributes of potato for salinity stress tolerance. Plant Physiology and Biochemistry. 203. 108036–108036. 8 indexed citations
11.
Watkins, Justin M., Timothy J Ross-Elliott, Xiaoyi Shan, et al.. (2021). Differential regulation of G protein signaling in Arabidopsis through two distinct pathways that internalize AtRGS1. Science Signaling. 14(695). 13 indexed citations
12.
Wang, Tao, Liang Wang, Yu Han, et al.. (2021). Adaptation of African swine fever virus to HEK293T cells. Transboundary and Emerging Diseases. 68(5). 2853–2866. 53 indexed citations
13.
Zhang, Chao, Junqiang Li, Ronglin Wang, et al.. (2021). Ese-3 Inhibits the Proliferation, Migration, and Invasion of HaCaT Cells by Downregulating PSIP1 and NUCKS1.. 51(4). 470–486. 3 indexed citations
14.
Yang, Xiangdong, Jing Yang, Lu Niu, et al.. (2020). Overexpression of the chitinase gene CmCH1 from Coniothyrium minitans renders enhanced resistance to Sclerotinia sclerotiorum in soybean. Transgenic Research. 29(2). 187–198. 31 indexed citations
15.
Yang, Xiangdong, Jing Yang, Hongli He, et al.. (2018). Enhanced resistance to sclerotinia stem rot in transgenic soybean that overexpresses a wheat oxalate oxidase. Transgenic Research. 28(1). 103–114. 28 indexed citations
16.
Meng, Xuan, Shijie Liu, Jing Yang, et al.. (2016). RPL23 Links Oncogenic RAS Signaling to p53-Mediated Tumor Suppression. Cancer Research. 76(17). 5030–5039. 24 indexed citations
17.
Gong, Yongfeng, Miao Yu, Jing Yang, et al.. (2014). The Cap1–claudin-4 regulatory pathway is important for renal chloride reabsorption and blood pressure regulation. Proceedings of the National Academy of Sciences. 111(36). E3766–74. 53 indexed citations
18.
Yang, Jing, Yuanyuan Li, Yixue Li, & Zhiqiang Ye. (2012). Partition dataset according to amino acid type improves the prediction of deleterious non-synonymous SNPs. Biochemical and Biophysical Research Communications. 419(1). 99–103. 1 indexed citations
19.
Leisewitz, Andrea V., et al.. (2010). Regulation of ENT1 expression and ENT1-dependent nucleoside transport by c-Jun N-terminal kinase. Biochemical and Biophysical Research Communications. 404(1). 370–375. 18 indexed citations
20.
Wang, M., Jing Yang, Shuai Liu, Zhengjun Xu, & K.-C. Chou. (2004). Weighted-support vector machines for predicting membrane protein types based on pseudo-amino acid composition. Protein Engineering Design and Selection. 17(6). 509–516. 155 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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