Shuzhu Tang

2.5k total citations · 1 hit paper
68 papers, 1.8k citations indexed

About

Shuzhu Tang is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Shuzhu Tang has authored 68 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 46 papers in Plant Science, 44 papers in Genetics and 22 papers in Molecular Biology. Recurrent topics in Shuzhu Tang's work include Genetic Mapping and Diversity in Plants and Animals (44 papers), GABA and Rice Research (20 papers) and Rice Cultivation and Yield Improvement (20 papers). Shuzhu Tang is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (44 papers), GABA and Rice Research (20 papers) and Rice Cultivation and Yield Improvement (20 papers). Shuzhu Tang collaborates with scholars based in China, Sudan and Hong Kong. Shuzhu Tang's co-authors include Minghong Gu, Qiaoquan Liu, Changjie Yan, Guohua Liang, Chuandeng Yi, Honggen Zhang, Zhixi Tian, Hengxiu Yu, Qian Qian and Jiayang Li and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Shuzhu Tang

65 papers receiving 1.8k citations

Hit Papers

Allelic diversities in rice starch biosynthesis lead to a... 2009 2026 2014 2020 2009 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shuzhu Tang China 18 1.5k 862 497 395 211 68 1.8k
Changjie Yan China 17 1.2k 0.8× 572 0.7× 403 0.8× 370 0.9× 176 0.8× 38 1.4k
Ayahiko Shomura Japan 14 2.5k 1.7× 1.8k 2.1× 273 0.5× 591 1.5× 98 0.5× 20 2.8k
W. D. Park United States 13 1.5k 1.0× 732 0.8× 156 0.3× 417 1.1× 93 0.4× 14 1.7k
Hongju Zhou China 7 1.9k 1.3× 1.2k 1.4× 142 0.3× 653 1.7× 65 0.3× 9 2.1k
P. S. Stinard United States 13 1.0k 0.7× 139 0.2× 405 0.8× 574 1.5× 153 0.7× 23 1.3k
Gopal Misra Philippines 17 1.2k 0.8× 340 0.4× 214 0.4× 247 0.6× 41 0.2× 24 1.3k
Reg Lance Australia 23 1.6k 1.0× 511 0.6× 149 0.3× 197 0.5× 60 0.3× 53 1.7k
Tokio Imbe Japan 22 1.2k 0.8× 582 0.7× 112 0.2× 227 0.6× 70 0.3× 44 1.3k
H. Miura Japan 26 2.3k 1.5× 658 0.8× 626 1.3× 229 0.6× 90 0.4× 77 2.6k
D. M. Wesenberg United States 19 1.4k 0.9× 578 0.7× 272 0.5× 155 0.4× 43 0.2× 63 1.5k

Countries citing papers authored by Shuzhu Tang

Since Specialization
Citations

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

Fields of papers citing papers by Shuzhu Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shuzhu Tang

This figure shows the co-authorship network connecting the top 25 collaborators of Shuzhu Tang. A scholar is included among the top collaborators of Shuzhu Tang 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 Shuzhu Tang. Shuzhu Tang 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.
Li, Meng, et al.. (2024). Development of introgression lines and mapping of qGW2, a novel QTL that confers grain width, in rice (Oryza sativa L.). Molecular Breeding. 44(2). 10–10. 1 indexed citations
2.
Zhang, Hu, et al.. (2020). Bioinformatics Analysis to Reveal Potential Differentially Expressed Long Non-Coding RNAs and Genes Associated with Tumour Metastasis in Lung Adenocarcinoma. SHILAP Revista de lepidopterología. 1 indexed citations
3.
Zhang, Changquan, Jihui Zhu, Xiaolei Fan, et al.. (2019). Wx, the Ancestral Allele of Rice Waxy Gene. Molecular Plant. 12(8). 1157–1166. 169 indexed citations
4.
Ahmed, Mohamed M., Meng Xu, Weiyun Wang, et al.. (2017). Transcriptional changes of rice in response to rice black-streaked dwarf virus. Gene. 628. 38–47. 10 indexed citations
5.
Zheng, Xingfei, Lanzhi Li, Fan Liang, et al.. (2017). Pedigree-based genome re-sequencing reveals genetic variation patterns of elite backbone varieties during modern rice improvement. Scientific Reports. 7(1). 292–292. 9 indexed citations
6.
Zhang, Honggen, Peng Li, Bo Li, et al.. (2011). Improving the Resistance of Wuyunjing 8 to Rice Stripe Virus via Molecular Marker-Assisted Selection. ACTA AGRONOMICA SINICA. 37(5). 745–754. 1 indexed citations
7.
Zhang, Hua, Qiang Zhao, Zhizhong Sun, et al.. (2011). Development and high-throughput genotyping of substitution lines carring the chromosome segments of indica 9311 in the background of japonica Nipponbare. Journal of genetics and genomics. 38(12). 603–611. 39 indexed citations
8.
Tang, Shuzhu, et al.. (2010). Application of HL type male sterile cytoplasm in japonica hybrid rice breeding.. Zhongguo shuidao kexue. 24(2). 116–124. 1 indexed citations
9.
Tang, Shuzhu, et al.. (2010). Analysis on combining ability of some characters between japonica CMS lines and restorer lines.. Journal of Yangzhou University. 31(1). 42–48. 1 indexed citations
10.
Yan, Changjie, Yu‐Wei Fang, Min Li, et al.. (2010). Effect of <I>PUL</I> Allelic Variation on Rice Cooking and Eating Quality. ACTA AGRONOMICA SINICA. 36(5). 728–735. 4 indexed citations
11.
Li, Bo, et al.. (2009). Improving Resistance of a Good-Quality japonica Variety Wuyujing 3 to Rice Stripe Virus via Molecular Marker-Assisted Selection. Zhongguo shuidao kexue. 23(3). 263–270. 3 indexed citations
12.
Tang, Shuzhu, et al.. (2009). Toward to apply HL type male sterile cytoplasm for Japonica heterosis utilization: consideration and practice.. Xi'nan nongye xuebao. 22(4). 1158–1164. 1 indexed citations
13.
Yu, Hengxiu, Quan‐Hong Yao, Ling Wang, et al.. (2009). Generation of selectable marker-free transgenic rice resistant to chewing insects using two co-transformation systems. Progress in Natural Science Materials International. 19(11). 1485–1492. 5 indexed citations
14.
Tang, Shuzhu, et al.. (2005). Comparison on the characteristics of the isonuclear alloplasmic CMS lines in japonica rice. Zhongguo shuidao kexue. 19(6). 521–526. 4 indexed citations
15.
Liu, Qiaoquan, et al.. (2002). Molecular marker for screening rice cultivars with intermediate amylose content in indica. 1 indexed citations
16.
Chen, Zongxiang, Tong YunHui, Shuzhu Tang, et al.. (2000). A preliminary study on resources of resistance to rice sheath blight. Zhongguo shuidao kexue. 14(1). 15–18. 7 indexed citations
17.
Li, Xin, et al.. (2000). Performance and Genetic Control of Quality Characters of Rice Grains in Japonica Hybrids. ACTA AGRONOMICA SINICA. 26(4). 411–419. 2 indexed citations
18.
Chen, Jian‐Min, Shiliang Gu, Shuzhu Tang, & Jufei Lu. (1998). Studies on recessive tall mutants in rice. Journal of Yangzhou University. 1(3). 36–41. 1 indexed citations
19.
Lu, Jufei, Changjie Yan, Shuzhu Tang, Minghong Gu, & Li Zhu. (1998). Genetic analysis of the wide compatiblity of rice variety huanuo from yunnan province. Journal of Yangzhou University. 1(4). 31–35. 1 indexed citations
20.
Tang, Shuzhu, et al.. (1990). Rice hybrids derived by crossing indica CMS lines with japonica widely compatible restorers.. Jiangsu nongye xuebao. 6(1). 10–16. 1 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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