Runshen Jiang

1.3k total citations
59 papers, 883 citations indexed

About

Runshen Jiang is a scholar working on Animal Science and Zoology, Genetics and Molecular Biology. According to data from OpenAlex, Runshen Jiang has authored 59 papers receiving a total of 883 indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Animal Science and Zoology, 22 papers in Genetics and 13 papers in Molecular Biology. Recurrent topics in Runshen Jiang's work include Animal Nutrition and Physiology (38 papers), Genetic and phenotypic traits in livestock (19 papers) and Livestock and Poultry Management (14 papers). Runshen Jiang is often cited by papers focused on Animal Nutrition and Physiology (38 papers), Genetic and phenotypic traits in livestock (19 papers) and Livestock and Poultry Management (14 papers). Runshen Jiang collaborates with scholars based in China, United States and Taiwan. Runshen Jiang's co-authors include Ning Yang, Zhaoyu Geng, Sihua Jin, Cheng Zhang, Lei Yang, Guixia Xu, Xing Guo, Xingyong Chen, Yi Wan and Lujiang Qu and has published in prestigious journals such as PLoS ONE, Scientific Reports and Poultry Science.

In The Last Decade

Runshen Jiang

51 papers receiving 847 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Runshen Jiang China 16 545 237 192 95 92 59 883
Jianmin Zou China 17 458 0.8× 124 0.5× 178 0.9× 105 1.1× 108 1.2× 50 824
G. S. Nattrass Australia 15 367 0.7× 193 0.8× 256 1.3× 50 0.5× 60 0.7× 28 775
Lilong An China 18 788 1.4× 118 0.5× 254 1.3× 90 0.9× 150 1.6× 58 1.3k
Congliang Ji China 11 360 0.7× 199 0.8× 257 1.3× 66 0.7× 43 0.5× 21 623
Kang–Nyeong Heo South Korea 15 600 1.1× 223 0.9× 147 0.8× 95 1.0× 61 0.7× 86 794
Lizhi Lu China 18 515 0.9× 229 1.0× 490 2.6× 158 1.7× 114 1.2× 111 1.2k
Monika Proszkowiec‐Weglarz United States 19 877 1.6× 100 0.4× 269 1.4× 83 0.9× 168 1.8× 57 1.3k
M. F. Miller United States 16 697 1.3× 243 1.0× 214 1.1× 177 1.9× 42 0.5× 42 1.0k
A. Geyra Israel 10 786 1.4× 144 0.6× 252 1.3× 74 0.8× 110 1.2× 10 1.1k
Birendra Mishra United States 19 937 1.7× 121 0.5× 239 1.2× 109 1.1× 252 2.7× 59 1.6k

Countries citing papers authored by Runshen Jiang

Since Specialization
Citations

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

Fields of papers citing papers by Runshen Jiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Runshen Jiang

This figure shows the co-authorship network connecting the top 25 collaborators of Runshen Jiang. A scholar is included among the top collaborators of Runshen Jiang 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 Runshen Jiang. Runshen Jiang 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.
2.
Sun, Tian‐Yu, et al.. (2025). Brief analysis of Nav1.7 inhibitors: Mechanism of action and new research trends. Bioorganic & Medicinal Chemistry. 128. 118180–118180.
3.
Chen, Hong, Weixin Liu, Hao Zhang, et al.. (2024). Effects of Bacillus subtilis KG109 on Growth Performance, Carcass Quality, Serum Indicators, Intestinal Morphology, and Digestive Enzymes in Broilers. Animals. 14(24). 3650–3650. 1 indexed citations
4.
Guo, Xing, Hao Wang, Meng Liu, et al.. (2024). Weighted gene co-expression network analysis identifies important modules and hub genes involved in the regulation of breast muscle yield in broilers. Animal Bioscience. 37(10). 1673–1682. 1 indexed citations
5.
Wang, Qiong, Xinye Zhang, Yalan Zhang, et al.. (2024). Molecular genetic foundation of a sex-linked tailless trait in Hongshan chicken by whole genome data analysis. Poultry Science. 103(6). 103685–103685. 1 indexed citations
6.
Wang, Hao, Hong Zhang, Yanan Liu, et al.. (2024). Identification of key module and hub genes affecting broiler body weight through weighted gene co-expression network analysis. Poultry Science. 103(11). 104111–104111. 1 indexed citations
7.
He, Xinxin, et al.. (2024). Identification of central regulators related to abdominal fat deposition in chickens based on weighted gene co-expression network analysis. Poultry Science. 103(3). 103436–103436. 6 indexed citations
8.
He, Xinxin, Yanan Liu, Xing Guo, et al.. (2024). Explorations on Key Module and Hub Genes Affecting IMP Content of Chicken Pectoralis Major Muscle Based on WGCNA. Animals. 14(3). 402–402. 4 indexed citations
9.
Guo, Xing, Hong Zhang, Hao Wang, et al.. (2023). Identification of Key Modules and Hub Genes Involved in Regulating the Color of Chicken Breast Meat Using WGCNA. Animals. 13(14). 2356–2356. 11 indexed citations
10.
Guo, Xing, Wei Wei, Bin Zhang, et al.. (2022). Genome-wide scan for selection signatures and genes related to heat tolerance in domestic chickens in the tropical and temperate regions in Asia. Poultry Science. 101(7). 101821–101821. 19 indexed citations
11.
Wan, Yi, et al.. (2019). Genetic parameters of the thick-to-thin albumen ratio and egg compositional traits in layer-type chickens. British Poultry Science. 60(5). 517–521. 11 indexed citations
12.
13.
Wan, Yi, et al.. (2017). Phenotypic characteristics of upright and pendulous comb among chicken breeds and association with growth rate and egg production. Animal Science Journal. 89(1). 250–256. 6 indexed citations
14.
Wan, Yi, et al.. (2017). RNA-Seq reveals seven promising candidate genes affecting the proportion of thick egg albumen in layer-type chickens. Scientific Reports. 7(1). 18083–18083. 21 indexed citations
15.
Zhang, Zebin, Changsheng Nie, Yaxiong Jia, et al.. (2016). Parallel Evolution of Polydactyly Traits in Chinese and European Chickens. PLoS ONE. 11(2). e0149010–e0149010. 18 indexed citations
16.
Sun, Hongyan, Runshen Jiang, Zebin Zhang, et al.. (2015). Transcriptome responses to heat stress in hypothalamus of a meat-type chicken. Journal of Animal Science and Biotechnology. 6(1). 6–6. 44 indexed citations
17.
Lien, Han‐Chung, Armando De Virgilio, Wen‐Chien Huang, et al.. (2014). Airway pH monitoring in patients with suspected obstructive sleep apnoea using the Dx‐pH oropharyngeal probe: preliminary report of a prospective cohort study. Clinical Otolaryngology. 39(6). 352–358. 6 indexed citations
18.
Jiang, Runshen, et al.. (2010). Broodiness, egg production, and correlations between broody traits in an indigenous chicken breed. Poultry Science. 89(6). 1094–1096. 27 indexed citations
19.
Yang, Ning & Runshen Jiang. (2005). Recent advances in breeding for quality chickens. World s Poultry Science Journal. 61(3). 373–381. 54 indexed citations
20.
Jiang, Runshen, et al.. (2005). Association of polymorphisms for prolactin and prolactin receptor genes with broody traits in chickens. Poultry Science. 84(6). 839–845. 88 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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