Sibin Yu

11.0k total citations · 5 hit papers
87 papers, 7.1k citations indexed

About

Sibin Yu is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Sibin Yu has authored 87 papers receiving a total of 7.1k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Plant Science, 69 papers in Genetics and 15 papers in Molecular Biology. Recurrent topics in Sibin Yu's work include Genetic Mapping and Diversity in Plants and Animals (69 papers), Rice Cultivation and Yield Improvement (51 papers) and GABA and Rice Research (33 papers). Sibin Yu is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (69 papers), Rice Cultivation and Yield Improvement (51 papers) and GABA and Rice Research (33 papers). Sibin Yu collaborates with scholars based in China, Philippines and United States. Sibin Yu's co-authors include Qifa Zhang, Yongzhong Xing, Caiguo Xu, Xianghua Li, Xianqing Liu, Hongyan Zhang, Liang Gong, Jie Luo, Hongju Zhou and Wensheng Wang and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Genetics and PLoS ONE.

In The Last Decade

Sibin Yu

84 papers receiving 7.0k citations

Hit Papers

A Novel Integrated Method for Large-Scale Detection, Iden... 2008 2026 2014 2020 2013 2008 2010 2014 2010 400 800 1.2k

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sibin Yu China 33 5.9k 3.8k 2.4k 312 268 87 7.1k
Hee‐Jong Koh South Korea 38 4.2k 0.7× 1.4k 0.4× 2.0k 0.9× 251 0.8× 150 0.6× 180 5.0k
Heiko C. Becker Germany 40 4.0k 0.7× 1.2k 0.3× 2.2k 0.9× 287 0.9× 674 2.5× 135 5.4k
Tatsuhito Fujimura Japan 36 4.9k 0.8× 605 0.2× 2.9k 1.2× 173 0.6× 149 0.6× 86 6.0k
Shozo Fujioka Japan 73 16.8k 2.8× 1.6k 0.4× 12.2k 5.1× 159 0.5× 333 1.2× 215 19.2k
Jong‐Seong Jeon South Korea 53 7.7k 1.3× 792 0.2× 4.4k 1.8× 126 0.4× 166 0.6× 191 8.9k
Yaakov Tadmor Israel 43 3.4k 0.6× 1.1k 0.3× 3.0k 1.3× 1.7k 5.5× 102 0.4× 99 5.9k
Arthur A. Schaffer Israel 39 3.3k 0.6× 692 0.2× 1.9k 0.8× 525 1.7× 49 0.2× 102 4.4k
Eyal Fridman Israel 29 2.1k 0.4× 646 0.2× 1.9k 0.8× 202 0.6× 122 0.5× 45 3.5k
Daniel Le Waters Australia 31 2.7k 0.5× 750 0.2× 1.0k 0.4× 81 0.3× 91 0.3× 92 3.7k

Countries citing papers authored by Sibin Yu

Since Specialization
Citations

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

Fields of papers citing papers by Sibin Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sibin Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Sibin Yu. A scholar is included among the top collaborators of Sibin Yu 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 Sibin Yu. Sibin Yu 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.
Yuan, Kai, et al.. (2025). Ghd7.1 improves eating quality by reducing grain protein content in rice. The Crop Journal. 13(4). 1145–1155.
2.
Yang, Xinyi, Juncheng Zhang, Lusheng Wang, et al.. (2025). BR signalling haplotypes contribute to indica–japonica differentiation for grain yield and quality in rice. Plant Biotechnology Journal. 23(5). 1618–1636. 1 indexed citations
3.
Qiu, Xianjin, et al.. (2023). Deciphering the Genetic Architecture of Color Variation in Whole Grain Rice by Genome-Wide Association. Plants. 12(4). 927–927. 6 indexed citations
4.
Sun, Wenqiang, et al.. (2023). A Structure Variation in qPH8.2 Detrimentally Affects Plant Architecture and Yield in Rice. Plants. 12(18). 3336–3336. 2 indexed citations
5.
Ye, Liang, Huihui Yu, Yating Li, et al.. (2023). A minimal genome design to maximally guarantee fertile inter-subspecific hybrid rice. Molecular Plant. 16(4). 726–738. 20 indexed citations
6.
Song, Bo, et al.. (2021). Mapping causal genes and genetic interactions for agronomic traits using a large F2 population in rice. G3 Genes Genomes Genetics. 11(11). 6 indexed citations
7.
Zhang, Zhenhua, et al.. (2021). Control of Thousand-Grain Weight by OsMADS56 in Rice. International Journal of Molecular Sciences. 23(1). 125–125. 23 indexed citations
8.
Xiong, Yin, Chaopu Zhang, Hongju Zhou, et al.. (2021). Identification of Heterotic Loci with Desirable Allelic Interaction to Increase Yield in Rice. Rice. 14(1). 97–97. 5 indexed citations
9.
Sun, Wenqiang, et al.. (2021). Identification of a novel QTL and candidate gene associated with grain size using chromosome segment substitution lines in rice. Scientific Reports. 11(1). 189–189. 18 indexed citations
10.
Niu, Xiaojun, et al.. (2020). Identification and Validation of Quantitative Trait Loci for Grain Number in Rice (Oryza sativa L.). Agronomy. 10(2). 180–180. 13 indexed citations
11.
Zhang, Chaopu, Jilin Wang, Qiang Sun, et al.. (2020). Genetic Dissection and Validation of Chromosomal Regions for Transmission Ratio Distortion in Intersubspecific Crosses of Rice. Frontiers in Plant Science. 11. 563548–563548. 3 indexed citations
12.
Chen, Jie, Wei Chen, Wenqiang Sun, et al.. (2018). Metabolome Analysis of Multi-Connected Biparental Chromosome Segment Substitution Line Populations. PLANT PHYSIOLOGY. 178(2). 612–625. 25 indexed citations
13.
Hu, Hui, Sibin Yu, Jianlong Xu, et al.. (2018). Characterization and fine mapping of two white panicle genes with duplicated effect in rice.. International Journal of Agriculture and Biology. 20(12). 2805–2811. 1 indexed citations
14.
Zhou, Hao, Pingbo Li, Weibo Xie, et al.. (2017). Genome-wide Association Analyses Reveal the Genetic Basis of Stigma Exsertion in Rice. Molecular Plant. 10(4). 634–644. 70 indexed citations
15.
Gong, Liang, Wei Chen, Yanqiang Gao, et al.. (2013). Genetic analysis of the metabolome exemplified using a rice population. Proceedings of the National Academy of Sciences. 110(50). 20320–20325. 137 indexed citations
16.
Wang, Jia, Huihui Yu, Weibo Xie, et al.. (2010). A global analysis of QTLs for expression variations in rice shoots at the early seedling stage. The Plant Journal. 63(6). 1063–1074. 56 indexed citations
17.
Yu, Sibin, et al.. (2008). Identification of QTLs for heat tolerance at flowering stage in rice. Zhongguo nongye Kexue. 28 indexed citations
18.
Xue, Weiya, Yongzhong Xing, Xiaoyu Weng, et al.. (2008). Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nature Genetics. 40(6). 761–767. 1246 indexed citations breakdown →
19.
Fan, Chuchuan, Sibin Yu, Chongrong Wang, & Yongzhong Xing. (2008). A causal C–A mutation in the second exon of GS3 highly associated with rice grain length and validated as a functional marker. Theoretical and Applied Genetics. 118(3). 465–472. 145 indexed citations
20.
Zhao, Lina, Hongju Zhou, Liaoxun Lu, et al.. (2008). Identification of quantitative trait loci controlling rice mature seed culturability using chromosomal segment substitution lines. Plant Cell Reports. 28(2). 247–256. 32 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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