Shi Shu

691 total citations
25 papers, 515 citations indexed

About

Shi Shu is a scholar working on Agronomy and Crop Science, Genetics and Molecular Biology. According to data from OpenAlex, Shi Shu has authored 25 papers receiving a total of 515 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Agronomy and Crop Science, 8 papers in Genetics and 6 papers in Molecular Biology. Recurrent topics in Shi Shu's work include Reproductive Physiology in Livestock (10 papers), Genetic and phenotypic traits in livestock (8 papers) and Ruminant Nutrition and Digestive Physiology (7 papers). Shi Shu is often cited by papers focused on Reproductive Physiology in Livestock (10 papers), Genetic and phenotypic traits in livestock (8 papers) and Ruminant Nutrition and Digestive Physiology (7 papers). Shi Shu collaborates with scholars based in China, Taiwan and United States. Shi Shu's co-authors include Chuang Xu, Cheng Xia, Lingwei Sun, Hongyou Zhang, Ling Wu, Longfei Wu, Zhi Jie Li, Hong Jin, Wendy Bailey and Holly Clouse and has published in prestigious journals such as Nature Biotechnology, Scientific Reports and Journal of Dairy Science.

In The Last Decade

Shi Shu

24 papers receiving 504 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shi Shu China 10 223 134 121 109 77 25 515
Jon A. Otero Spain 12 18 0.1× 187 1.4× 40 0.3× 114 1.0× 18 0.2× 13 424
G Oriani Italy 17 36 0.2× 57 0.4× 25 0.2× 123 1.1× 266 3.5× 33 675
Chunlei Zhang China 16 20 0.1× 126 0.9× 48 0.4× 212 1.9× 22 0.3× 61 757
Margaret Frazer United States 12 28 0.1× 46 0.3× 29 0.2× 137 1.3× 89 1.2× 23 806
Jérôme M. Giraudel Switzerland 18 31 0.1× 44 0.3× 9 0.1× 101 0.9× 107 1.4× 23 917
Philip Burnett United States 6 14 0.1× 9 0.1× 88 0.7× 63 0.6× 89 1.2× 12 355
Yiqin Huang China 13 19 0.1× 166 1.2× 13 0.1× 103 0.9× 7 0.1× 38 616
J. A. Guzmán Argentina 12 14 0.1× 13 0.1× 113 0.9× 179 1.6× 21 0.3× 23 555
Narender Kumar India 10 86 0.4× 50 0.4× 2 0.0× 94 0.9× 42 0.5× 28 441
J.L. PIMENTEL United States 10 12 0.1× 13 0.1× 97 0.8× 77 0.7× 139 1.8× 13 470

Countries citing papers authored by Shi Shu

Since Specialization
Citations

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

Fields of papers citing papers by Shi Shu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shi Shu

This figure shows the co-authorship network connecting the top 25 collaborators of Shi Shu. A scholar is included among the top collaborators of Shi Shu 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 Shi Shu. Shi Shu 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, Ping, Huahu Ye, Feng Guo, et al.. (2024). Construction of cynomolgus monkey type 2 diabetes models by combining genetic prediction model with high-energy diet. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1871(3). 167616–167616.
2.
Mipam, Tserang Donko, Peng Zhang, Peng Wei, et al.. (2023). Transcriptomic analysis of yak longissimus dorsi muscle identifies genes associated with tenderness. Animal Biotechnology. 34(8). 1–10. 3 indexed citations
3.
Wang, Hui, et al.. (2023). IMF deposition ceRNA network analysis and functional study of HIF1a in yak. Frontiers in Veterinary Science. 10. 1272238–1272238. 6 indexed citations
4.
Shu, Shi, et al.. (2019). Plasma Protein Comparison between Dairy Cows with Inactive Ovaries and Estrus. Scientific Reports. 9(1). 13709–13709. 19 indexed citations
5.
Zhang, Jiang, et al.. (2018). 1H NMR plasma metabolomic profiling of ovarian quiescence in energy balanced postpartum dairy cows. Veterinary Quarterly. 38(1). 47–52. 8 indexed citations
6.
Shu, Shi, et al.. (2017). Plasma metabolic profiling of postpartum dairy cows with inactive ovaries based on GC/MS technique.. Zhongguo nongye Kexue. 50(15). 3042–3051. 1 indexed citations
7.
Shu, Shi, Changsheng Li, Cheng Xia, et al.. (2017). Investigation of negative energy balance and postpartum anoestrus in an intensive dairy farm from the Chinese province of Heilongjiang. Acta Veterinaria Brno. 86(1). 59–65. 3 indexed citations
8.
Shu, Shi, Gang Wang, Yunlong Bai, et al.. (2017). Protein profiling of plasma proteins in dairy cows with subclinical hypocalcaemia. Irish Veterinary Journal. 70(1). 3–3. 8 indexed citations
9.
Sun, Lingwei, et al.. (2016). Nuclear magnetic resonance-based serum metabolic profiling of dairy cows with footrot. Journal of Veterinary Medical Science. 78(9). 1421–1428. 11 indexed citations
10.
Xia, Cheng, Gang Wang, Shi Shu, et al.. (2016). Metabolic profiles using 1H-nuclear magnetic resonance spectroscopy in postpartum dairy cows with ovarian inactivity. Theriogenology. 86(6). 1475–1481. 16 indexed citations
11.
Shu, Shi, Yunlong Bai, Gang Wang, et al.. (2016). Differentially expressed serum proteins associated with calcium regulation and hypocalcemia in dairy cows. Asian-Australasian Journal of Animal Sciences. 30(6). 893–901. 2 indexed citations
12.
Shu, Shi, et al.. (2016). 2-DE-MS based proteomic investigation of dairy cows with footrot. Journal of Veterinary Research. 60(1). 63–69. 2 indexed citations
13.
Xu, Chuang, et al.. (2015). Mass spectral analysis of urine proteomic profiles of dairy cows suffering from clinical ketosis. Veterinary Quarterly. 35(3). 133–141. 14 indexed citations
14.
Wang, Bo, Shi Shu, Hongyou Zhang, et al.. (2015). Critical thresholds of liver function parameters for ketosis prediction in dairy cows using receiver operating characteristic (ROC) analysis. Veterinary Quarterly. 35(3). 159–164. 28 indexed citations
15.
Shu, Shi, et al.. (2015). Protein expression in dairy cows with and without subclinical hypocalcaemia. New Zealand Veterinary Journal. 64(2). 101–106. 9 indexed citations
16.
Sun, Lingwei, et al.. (2014). 1H-Nuclear magnetic resonance-based plasma metabolic profiling of dairy cows with clinical and subclinical ketosis. Journal of Dairy Science. 97(3). 1552–1562. 82 indexed citations
17.
Zhang, Hongyou, Ling Wu, Chuang Xu, et al.. (2013). Plasma metabolomic profiling of dairy cows affected with ketosis using gas chromatography/mass spectrometry. BMC Veterinary Research. 9(1). 186–186. 62 indexed citations
18.
Xia, Cheng, Longfei Wu, Chuang Xu, et al.. (2011). Proteomic analysis of plasma from cows affected with milk fever using two-dimensional differential in-gel electrophoresis and mass spectrometry. Research in Veterinary Science. 93(2). 857–861. 14 indexed citations
19.
Yu, Yan, Hong Jin, Daniel Holder, et al.. (2010). Urinary biomarkers trefoil factor 3 and albumin enable early detection of kidney tubular injury. Nature Biotechnology. 28(5). 470–477. 147 indexed citations
20.
Shu, Shi. (1991). Fate of N from Green Manures and Ammonium Sulfate. 土壤圈(英文版). 2 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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