Shouye Liu

545 total citations · 1 hit paper
10 papers, 192 citations indexed

About

Shouye Liu is a scholar working on Genetics, Molecular Biology and Plant Science. According to data from OpenAlex, Shouye Liu has authored 10 papers receiving a total of 192 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Genetics, 3 papers in Molecular Biology and 2 papers in Plant Science. Recurrent topics in Shouye Liu's work include Genetic Associations and Epidemiology (3 papers), Genetic and phenotypic traits in livestock (3 papers) and Genetics and Plant Breeding (2 papers). Shouye Liu is often cited by papers focused on Genetic Associations and Epidemiology (3 papers), Genetic and phenotypic traits in livestock (3 papers) and Genetics and Plant Breeding (2 papers). Shouye Liu collaborates with scholars based in China, Australia and United States. Shouye Liu's co-authors include Jian Zeng, Jian Yang, Ting Qi, Hailing Fang, Yang Wu, Futao Zhang, Zhili Zheng, Weixun Wu, Ilja M. Nolte and Julia Sidorenko and has published in prestigious journals such as Nature Genetics, Scientific Reports and The American Journal of Human Genetics.

In The Last Decade

Shouye Liu

10 papers receiving 191 citations

Hit Papers

Leveraging functional genomic annotations and genome cove... 2024 2026 2025 2024 10 20 30

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shouye Liu China 6 108 81 31 16 15 10 192
Zhanye Zheng China 6 107 1.0× 159 2.0× 16 0.5× 25 1.6× 20 1.3× 7 255
J. N. Hirschhorn United States 3 189 1.8× 126 1.6× 19 0.6× 21 1.3× 12 0.8× 4 283
Danning Li China 7 27 0.3× 69 0.9× 28 0.9× 9 0.6× 5 0.3× 13 129
Wai‐Ki Yip United States 6 92 0.9× 57 0.7× 6 0.2× 10 0.6× 22 1.5× 14 180
Yoko Hiraki Japan 10 131 1.2× 97 1.2× 18 0.6× 11 0.7× 6 0.4× 17 219
Emanuela Ponzi Italy 7 93 0.9× 87 1.1× 17 0.5× 5 0.3× 16 1.1× 14 182
Mathijs Kattenberg Netherlands 4 65 0.6× 41 0.5× 5 0.2× 15 0.9× 17 1.1× 6 126
Celine L. St. Pierre United States 9 86 0.8× 61 0.8× 10 0.3× 10 0.6× 28 1.9× 15 167
R. Wilson United States 3 168 1.6× 199 2.5× 24 0.8× 7 0.4× 52 3.5× 3 344
James M.J. Lawlor United States 9 95 0.9× 60 0.7× 8 0.3× 18 1.1× 8 0.5× 16 176

Countries citing papers authored by Shouye Liu

Since Specialization
Citations

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

Fields of papers citing papers by Shouye Liu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shouye Liu

This figure shows the co-authorship network connecting the top 25 collaborators of Shouye Liu. A scholar is included among the top collaborators of Shouye Liu 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 Shouye Liu. Shouye Liu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

10 of 10 papers shown
1.
Zheng, Zhili, Shouye Liu, Julia Sidorenko, et al.. (2024). Leveraging functional genomic annotations and genome coverage to improve polygenic prediction of complex traits within and between ancestries. Nature Genetics. 56(5). 767–777. 38 indexed citations breakdown →
2.
Qi, Ting, Shihao Zhu, Yunsheng Li, et al.. (2023). Maternal aging increases offspring adult body size via transmission of donut-shaped mitochondria. Cell Research. 33(11). 821–834. 11 indexed citations
3.
Ross, Elizabeth M., Xianming Wei, Shouye Liu, et al.. (2023). Use of continuous genotypes for genomic prediction in sugarcane. The Plant Genome. 17(1). e20417–e20417. 6 indexed citations
4.
Li, Ang, Shouye Liu, Andrew Bakshi, et al.. (2023). mBAT-combo: A more powerful test to detect gene-trait associations from GWAS data. The American Journal of Human Genetics. 110(1). 30–43. 13 indexed citations
5.
Zeng, Jian, Zhili Zheng, Shouye Liu, et al.. (2023). LEVERAGING FUNCTIONAL GENOMIC ANNOTATIONS AND GENOME COVERAGE TO IMPROVE POLYGENIC PREDICTION OF COMPLEX TRAITS WITHIN AND BETWEEN ANCESTRIES. European Neuropsychopharmacology. 75. S29–S30. 1 indexed citations
6.
Qi, Ting, Yang Wu, Hailing Fang, et al.. (2022). Genetic control of RNA splicing and its distinct role in complex trait variation. Nature Genetics. 54(9). 1355–1363. 96 indexed citations
7.
Qi, Ting, et al.. (2022). Genetic control of RNA splicing and its distinct role in complex trait variation - code. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
8.
Meng, Guanglei, et al.. (2019). UAV Attack and Defense Optimization Guidance Method Based on Target Trajectory Prediction. 495–499. 2 indexed citations
9.
Zhou, Liyuan, Shouye Liu, Weixun Wu, et al.. (2016). Dissection of genetic architecture of rice plant height and heading date by multiple-strategy-based association studies. Scientific Reports. 6(1). 29718–29718. 23 indexed citations
10.
Wang, Yanong, et al.. (1996). Investigation of relationships between KI-67 score, DNA index, and histologic grade in soft tissue sarcomas. Chinese Journal of Cancer Research. 8(1). 55–59. 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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