Jinliang Yang

3.5k total citations · 1 hit paper
59 papers, 1.9k citations indexed

About

Jinliang Yang is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Jinliang Yang has authored 59 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Plant Science, 28 papers in Genetics and 14 papers in Molecular Biology. Recurrent topics in Jinliang Yang's work include Genetic Mapping and Diversity in Plants and Animals (28 papers), Genetics and Plant Breeding (17 papers) and Genetic and phenotypic traits in livestock (13 papers). Jinliang Yang is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (28 papers), Genetics and Plant Breeding (17 papers) and Genetic and phenotypic traits in livestock (13 papers). Jinliang Yang collaborates with scholars based in United States, China and Germany. Jinliang Yang's co-authors include James C. Schnable, Jeffrey Ross‐Ibarra, Bing Yang, Wolf B. Frommer, Davide Sosso, Gen Xu, Chenyong Miao, Yufeng Ge, Patrick S. Schnable and Masaharu Suzuki and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Nature Genetics.

In The Last Decade

Jinliang Yang

57 papers receiving 1.9k citations

Hit Papers

Seed filling in domesticated maize and rice depends on SW... 2015 2026 2018 2022 2015 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
Jinliang Yang United States 22 1.5k 584 497 212 121 59 1.9k
Vincent Segura France 22 1.9k 1.3× 1.1k 2.0× 794 1.6× 148 0.7× 122 1.0× 54 2.7k
Malia Gehan United States 16 1.3k 0.8× 242 0.4× 366 0.7× 393 1.9× 81 0.7× 29 1.6k
Fiona Corke United Kingdom 22 1.7k 1.1× 243 0.4× 756 1.5× 118 0.6× 136 1.1× 49 1.9k
Nathan D. Miller United States 23 1.8k 1.2× 195 0.3× 987 2.0× 133 0.6× 89 0.7× 40 2.0k
Jiuran Zhao China 27 1.9k 1.3× 738 1.3× 832 1.7× 105 0.5× 254 2.1× 114 2.5k
Aluízio Borém Brazil 24 1.5k 1.0× 362 0.6× 238 0.5× 123 0.6× 262 2.2× 130 1.8k
Myriam Dauzat France 21 1.6k 1.1× 163 0.3× 501 1.0× 180 0.8× 115 1.0× 30 1.9k
Christoph Grieder Switzerland 13 911 0.6× 565 1.0× 248 0.5× 139 0.7× 164 1.4× 24 1.2k
Josquin Tibbits Australia 21 1.1k 0.8× 487 0.8× 431 0.9× 137 0.6× 150 1.2× 52 1.6k
Waseem Hussain United States 13 982 0.7× 299 0.5× 247 0.5× 126 0.6× 87 0.7× 38 1.1k

Countries citing papers authored by Jinliang Yang

Since Specialization
Citations

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

Fields of papers citing papers by Jinliang Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jinliang Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Jinliang Yang. A scholar is included among the top collaborators of Jinliang 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 Jinliang Yang. Jinliang 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.
Zhang, Zhixiong, Wei Xie, Qiang Huang, et al.. (2025). Discovery of STING antagonists targeting cGAS-STING pathway to alleviate IMQ-induced psoriasis-like dermatitis. European Journal of Pharmaceutical Sciences. 210. 107091–107091. 1 indexed citations
2.
Wang, Yiru, Cheng He, Qiqi Wang, et al.. (2025). Large DNA and protein language models enhance discovery of deleterious mutations in maize. Genome biology. 26(1). 412–412.
3.
Mukhtar, Hussnain, et al.. (2024). Nitrogen input differentially shapes the rhizosphere microbiome diversity and composition across diverse maize lines. Biology and Fertility of Soils. 61(1). 1–12. 4 indexed citations
5.
He, Cheng, Jacob D. Washburn, Heidi F. Kaeppler, et al.. (2024). Trait association and prediction through integrative k ‐mer analysis. The Plant Journal. 120(2). 833–850. 1 indexed citations
6.
Mural, Ravi V., et al.. (2024). Dissecting the genetic architecture of sunflower disc diameter using genome‐wide association study. Plant Direct. 8(10). e70010–e70010. 1 indexed citations
7.
Li, Delin, et al.. (2024). TWAS facilitates gene-scale trait genetic dissection through gene expression, structural variations, and alternative splicing in soybean. Plant Communications. 5(10). 101010–101010. 6 indexed citations
8.
Wijewardane, Nuwan K., Huichun Zhang, Jinliang Yang, et al.. (2023). A leaf-level spectral library to support high-throughput plant phenotyping: predictive accuracy and model transfer. Journal of Experimental Botany. 74(14). 4050–4062. 15 indexed citations
9.
Ge, Yufeng, et al.. (2023). Leaf-Counting in Monocot Plants Using Deep Regression Models. Sensors. 23(4). 1890–1890. 6 indexed citations
10.
Cheng, Hao, et al.. (2023). Microbiome-enabled genomic selection improves prediction accuracy for nitrogen-related traits in maize. G3 Genes Genomes Genetics. 14(3). 3 indexed citations
11.
Meier, Michael, Gen Xu, Martha Lopez‐Guerrero, et al.. (2022). Association analyses of host genetics, root-colonizing microbes, and plant phenotypes under different nitrogen conditions in maize. eLife. 11. 27 indexed citations
13.
Deng, Siwen, et al.. (2021). Genome wide association study reveals plant loci controlling heritability of the rhizosphere microbiome. The ISME Journal. 15(11). 3181–3194. 135 indexed citations
14.
Meier, Michael, Martha Lopez‐Guerrero, Marty R. Schmer, et al.. (2021). Rhizosphere Microbiomes in a Historical Maize-Soybean Rotation System Respond to Host Species and Nitrogen Fertilization at the Genus and Subgenus Levels. Applied and Environmental Microbiology. 87(12). e0313220–e0313220. 25 indexed citations
15.
Samayoa, Luis Fernando, Bode A. Olukolu, Chin Jian Yang, et al.. (2021). Domestication reshaped the genetic basis of inbreeding depression in a maize landrace compared to its wild relative, teosinte. PLoS Genetics. 17(12). e1009797–e1009797. 9 indexed citations
16.
Miao, Chenyong, Jinliang Yang, & James C. Schnable. (2018). Optimising the identification of causal variants across varying genetic architectures in crops. Plant Biotechnology Journal. 17(5). 893–905. 33 indexed citations
17.
Bezrutczyk, Margaret, Thomas Hartwig, Si Nian Char, et al.. (2018). Impaired phloem loading in zmsweet13a,b,c sucrose transporter triple knock‐out mutants in Zea mays. New Phytologist. 218(2). 594–603. 134 indexed citations
18.
Dong, Zhaobin, Wei Li, Erica Unger‐Wallace, et al.. (2017). Ideal crop plant architecture is mediated by tassels replace upper ears1, a BTB/POZ ankyrin repeat gene directly targeted by TEOSINTE BRANCHED1. Proceedings of the National Academy of Sciences. 114(41). E8656–E8664. 75 indexed citations
19.
Yang, Jinliang, Sofiane Mezmouk, Andy Baumgarten, et al.. (2017). Incomplete dominance of deleterious alleles contributes substantially to trait variation and heterosis in maize. PLoS Genetics. 13(9). e1007019–e1007019. 121 indexed citations
20.
Yang, Jinliang, Jinliang Yang, Yangping Wu, et al.. (2014). Polymorphisms in CISH Gene Are Associated with Persistent Hepatitis B Virus Infection in Han Chinese Population. PLoS ONE. 9(6). e100826–e100826. 14 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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