Yaolong Yang

1.8k total citations · 1 hit paper
34 papers, 1.2k citations indexed

About

Yaolong Yang is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Yaolong Yang has authored 34 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Plant Science, 23 papers in Genetics and 6 papers in Molecular Biology. Recurrent topics in Yaolong Yang's work include Genetic Mapping and Diversity in Plants and Animals (23 papers), Rice Cultivation and Yield Improvement (14 papers) and GABA and Rice Research (13 papers). Yaolong Yang is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (23 papers), Rice Cultivation and Yield Improvement (14 papers) and GABA and Rice Research (13 papers). Yaolong Yang collaborates with scholars based in China and United States. Yaolong Yang's co-authors include Dali Zeng, Jiang Hu, Qian Qian, Longbiao Guo, Yuchun Rao, Li Zhu, Guangheng Zhang, Xinghua Wei, Qun Xu and Feng Yue and has published in prestigious journals such as Advanced Functional Materials, PLANT PHYSIOLOGY and The Plant Journal.

In The Last Decade

Yaolong Yang

33 papers receiving 1.1k citations

Hit Papers

Rational design of high-yield and superior-quality rice 2017 2026 2020 2023 2017 50 100 150 200 250

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yaolong Yang China 16 1.0k 581 313 59 47 34 1.2k
Muhammad Abdul Rehman Rashid Pakistan 17 1.0k 1.0× 414 0.7× 298 1.0× 30 0.5× 19 0.4× 79 1.2k
Ranjith Kumar Ellur India 19 1.4k 1.4× 539 0.9× 174 0.6× 31 0.5× 28 0.6× 96 1.5k
Haitao Zhu China 23 1.9k 1.9× 1.4k 2.4× 486 1.6× 86 1.5× 49 1.0× 64 2.1k
Anming Ding China 19 1.3k 1.3× 468 0.8× 386 1.2× 29 0.5× 44 0.9× 45 1.4k
Kehui Zhan China 18 935 0.9× 208 0.4× 304 1.0× 43 0.7× 37 0.8× 51 1.1k
Jai Prakash Jaiswal India 13 824 0.8× 188 0.3× 196 0.6× 34 0.6× 34 0.7× 63 916
Dangqun Cui China 24 1.5k 1.4× 471 0.8× 384 1.2× 74 1.3× 46 1.0× 73 1.7k
Zhongmin Han China 18 805 0.8× 518 0.9× 235 0.8× 18 0.3× 14 0.3× 28 911
Xiangqian Zhao China 15 725 0.7× 310 0.5× 122 0.4× 110 1.9× 14 0.3× 23 812
Samuel C. Chukwu Malaysia 15 840 0.8× 190 0.3× 94 0.3× 32 0.5× 100 2.1× 40 1.0k

Countries citing papers authored by Yaolong Yang

Since Specialization
Citations

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

Fields of papers citing papers by Yaolong Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yaolong Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Yaolong Yang. A scholar is included among the top collaborators of Yaolong 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 Yaolong Yang. Yaolong 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, Yunfei, Yaolong Yang, Jun Lin, et al.. (2025). A Water State Manipulation Strategy for Ultra‐Stiff Yet Highly Sensitive Hydrogels. Advanced Functional Materials. 35(26). 9 indexed citations
2.
Yang, Yaolong, Qun Xu, Feng Yue, et al.. (2025). A comparative study highlights superiority of LSTM in crop genomic prediction. Planta. 262(6). 125–125. 1 indexed citations
3.
Ma, Xiaoding, Shengyang Wu, Di Cui, et al.. (2024). DeepCCR: large‐scale genomics‐based deep learning method for improving rice breeding. Plant Biotechnology Journal. 22(10). 2691–2693. 19 indexed citations
4.
Yang, Yaolong, Dandan Zheng, Lili Luo, et al.. (2023). Mussel-inspired hydrogels with UCST for temperature-controlled reversible adhesion. Giant. 16. 100182–100182. 7 indexed citations
5.
Ye, Jing, Yingying Yang, Xiaoping Yuan, et al.. (2022). Identification of SMG3, a QTL Coordinately Controls Grain Size, Grain Number per Panicle, and Grain Weight in Rice. Frontiers in Plant Science. 13. 880919–880919. 12 indexed citations
6.
Yue, Feng, Xiaoping Yuan, Yiping Wang, et al.. (2021). Validation of a QTL for Grain Size and Weight Using an Introgression Line from a Cross between Oryza sativa and Oryza minuta. Rice. 14(1). 43–43. 12 indexed citations
7.
Xu, Xin, Junhua Ye, Yingying Yang, et al.. (2020). Genome-Wide Association Study of Rice Rooting Ability at the Seedling Stage. Rice. 13(1). 59–59. 14 indexed citations
8.
Niu, Xiaojun, Yaolong Yang, Wang Shan, et al.. (2018). Divergent Hd1, Ghd7, and DTH7 Alleles Control Heading Date and Yield Potential of Japonica Rice in Northeast China. Frontiers in Plant Science. 9. 35–35. 43 indexed citations
9.
Zhang, Mengchen, Jing Ye, Qun Xu, et al.. (2018). Genome-wide association study of cold tolerance of Chinese indica rice varieties at the bud burst stage. Plant Cell Reports. 37(3). 529–539. 37 indexed citations
10.
Yang, Yaolong, Xinghua Wei, Xiaojun Niu, et al.. (2018). PGL3 is required for chlorophyll synthesis and impacts leaf senescence in rice. Journal of Zhejiang University SCIENCE B. 19(4). 263–273. 9 indexed citations
11.
Zeng, Dali, Zhixi Tian, Yuchun Rao, et al.. (2017). Rational design of high-yield and superior-quality rice. Nature Plants. 3(4). 17031–17031. 292 indexed citations breakdown →
12.
Yue, Feng, Qing Lu, Rongrong Zhai, et al.. (2016). Genome wide association mapping for grain shape traits in indica rice. Planta. 244(4). 819–830. 37 indexed citations
13.
Xu, Qun, Xiaoping Yuan, Shan Wang, et al.. (2016). The genetic diversity and structure of indica rice in China as detected by single nucleotide polymorphism analysis. BMC Genetics. 17(1). 53–53. 39 indexed citations
14.
Yang, Yaolong, Jie Xu, Lichao Huang, et al.. (2015). PGL, encoding chlorophyllide a oxygenase 1, impacts leaf senescence and indirectly affects grain yield and quality in rice. Journal of Experimental Botany. 67(5). 1297–1310. 110 indexed citations
15.
Rao, Yuchun, Yaolong Yang, Jie Xu, et al.. (2015). EARLY SENESCENCE1 Encodes a SCAR-LIKE PROTEIN2 That Affects Water Loss in Rice. PLANT PHYSIOLOGY. 169(2). 1225–1239. 42 indexed citations
17.
Zhang, Guangheng, Shuyu Li, Li Wang, et al.. (2014). LSCHL4 from Japonica Cultivar, Which Is Allelic to NAL1 , Increases Yield of Indica Super Rice 93-11. Molecular Plant. 7(8). 1350–1364. 115 indexed citations
18.
Rao, Yuchun, Yaolong Yang, Dedong Xin, et al.. (2013). Characterization and cloning of a brittle culm mutant (bc88) in rice (Oryza sativa L.). Chinese Science Bulletin. 58(24). 3000–3006. 9 indexed citations
19.
Su, Yan, Yuchun Rao, Shikai Hu, et al.. (2011). Map-based cloning proves qGC-6, a major QTL for gel consistency of japonica/indica cross, responds by Waxy in rice (Oryza sativa L.). Theoretical and Applied Genetics. 123(5). 859–867. 68 indexed citations
20.
Yang, Yaolong, Yuchun Rao, Huijuan Liu, et al.. (2011). Characterization and fine mapping of an early senescence mutant (es-t) in Oryza sativa L.. Chinese Science Bulletin. 56(23). 2437–2443. 7 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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