Junhu Yao

2.1k total citations · 1 hit paper
61 papers, 1.3k citations indexed

About

Junhu Yao is a scholar working on Agronomy and Crop Science, Molecular Biology and Genetics. According to data from OpenAlex, Junhu Yao has authored 61 papers receiving a total of 1.3k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Agronomy and Crop Science, 27 papers in Molecular Biology and 9 papers in Genetics. Recurrent topics in Junhu Yao's work include Ruminant Nutrition and Digestive Physiology (35 papers), Gut microbiota and health (20 papers) and Reproductive Physiology in Livestock (16 papers). Junhu Yao is often cited by papers focused on Ruminant Nutrition and Digestive Physiology (35 papers), Gut microbiota and health (20 papers) and Reproductive Physiology in Livestock (16 papers). Junhu Yao collaborates with scholars based in China, United States and Sweden. Junhu Yao's co-authors include Yangchun Cao, Xiaojun Yang, Shengru Wu, Zongjun Li, Xuemei Nan, Xiaofei Wang, Benhai Xiong, Linshu Jiang, Jing Shen and Xiaojun Yang and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied and Environmental Microbiology and Journal of Agricultural and Food Chemistry.

In The Last Decade

Junhu Yao

57 papers receiving 1.3k citations

Hit Papers

Multi-omics revealed the long-term effect of ruminal keys... 2023 2026 2024 2025 2023 20 40 60

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Junhu Yao China 22 637 516 258 256 211 61 1.3k
Muhammad Zahoor Khan China 24 563 0.9× 386 0.7× 286 1.1× 307 1.2× 337 1.6× 145 1.7k
Benhai Xiong China 24 882 1.4× 564 1.1× 289 1.1× 255 1.0× 198 0.9× 66 1.5k
Xuemei Nan China 22 709 1.1× 618 1.2× 352 1.4× 207 0.8× 184 0.9× 67 1.6k
Naifeng Zhang China 25 953 1.5× 540 1.0× 463 1.8× 226 0.9× 342 1.6× 100 1.8k
Ahmed A. Elolimy Egypt 22 430 0.7× 398 0.8× 266 1.0× 114 0.4× 230 1.1× 82 1.3k
Quanhui Peng China 25 848 1.3× 404 0.8× 490 1.9× 152 0.6× 274 1.3× 103 1.6k
Shengru Wu China 18 312 0.5× 401 0.8× 190 0.7× 104 0.4× 154 0.7× 68 957
Kamila Puppel Poland 19 472 0.7× 272 0.5× 330 1.3× 228 0.9× 281 1.3× 79 1.3k
Yulin Ma China 18 256 0.4× 257 0.5× 165 0.6× 178 0.7× 173 0.8× 46 928
Sachinandan De India 23 331 0.5× 829 1.6× 290 1.1× 349 1.4× 555 2.6× 151 2.0k

Countries citing papers authored by Junhu Yao

Since Specialization
Citations

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

Fields of papers citing papers by Junhu Yao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junhu Yao

This figure shows the co-authorship network connecting the top 25 collaborators of Junhu Yao. A scholar is included among the top collaborators of Junhu Yao 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 Junhu Yao. Junhu Yao 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
2.
Zhang, Xintong, Shuaishuai Li, Jingbo Ma, et al.. (2025). Effect of fumarate and live yeast on ruminal fermentation, methane emissions, and blood metabolites in dairy goats. Journal of Dairy Science. 108(10). 10855–10868.
4.
Ni, Li, Jingyi Xu, Xiaojun Yang, et al.. (2025). Noncoding RNAs in Host–Microbiota Interaction. SHILAP Revista de lepidopterología. 3(4). 358–367.
5.
Yang, Hao, Javier Villafuerte-Gálvez, Jing Lu, et al.. (2025). Pterostilbene attenuates intestinal barrier damage and secondary liver oxidative stress in a murine model of Clostridium difficile infection by regulating the gut microbiota. Food & Function. 16(9). 3325–3343. 1 indexed citations
6.
Wang, La-Mei, Xinhua Chen, Nira R. Pollock, et al.. (2025). Metagenomic analysis reveals distinct patterns of gut microbiota features with diversified functions in C. difficile infection (CDI), asymptomatic carriage and non-CDI diarrhea. Gut Microbes. 17(1). 2505269–2505269. 2 indexed citations
7.
Wang, Mengya, Lei Zhang, Xingwei Jiang, et al.. (2024). Multiomics analysis revealed that the metabolite profile of raw milk is associated with the lactation stage of dairy cows and could be affected by variations in the ruminal microbiota. Journal of Dairy Science. 107(10). 8709–8721. 7 indexed citations
8.
Xu, Jingyi, Anguo Liu, Xinmei Li, et al.. (2024). Integrating genome‐ and transcriptome‐wide association studies to uncover the host–microbiome interactions in bovine rumen methanogenesis. SHILAP Revista de lepidopterología. 3(5). e234–e234. 9 indexed citations
9.
Zhang, Jun, Huifeng Liu, Lei Li, et al.. (2024). Altered bile acid and correlations with gut microbiome in transition dairy cows with different glucose and lipid metabolism status. Journal of Dairy Science. 107(11). 9915–9933. 9 indexed citations
10.
Wang, Dangdang, Luyu Chen, Junjian Yu, et al.. (2023). Multi-omics revealed the long-term effect of ruminal keystone bacteria and the microbial metabolome on lactation performance in adult dairy goats. Microbiome. 11(1). 215–215. 60 indexed citations breakdown →
11.
Wang, Lamei, Shanlin Ke, Xinhua Chen, et al.. (2022). Yak rumen microbiome elevates fiber degradation ability and alters rumen fermentation pattern to increase feed efficiency. Animal nutrition. 11. 201–214. 46 indexed citations
12.
Zhao, Yiguang, Yue Wang, Xuemei Nan, et al.. (2022). Responses of Lactation, Rumen Fermentation and Blood Biochemical Parameters with Increasing Dietary Inulin Supplementation in Mid-Lactation Dairy Cows. Agriculture. 12(4). 521–521. 6 indexed citations
13.
Cao, Yangchun, Dangdang Wang, Lamei Wang, et al.. (2021). Physically effective neutral detergent fiber improves chewing activity, rumen fermentation, plasma metabolites, and milk production in lactating dairy cows fed a high-concentrate diet. Journal of Dairy Science. 104(5). 5631–5642. 28 indexed citations
14.
Guo, Long, Junhu Yao, & Yangchun Cao. (2021). Regulation of pancreatic exocrine in ruminants and the related mechanism: The signal transduction and more. Animal nutrition. 7(4). 1145–1151. 8 indexed citations
17.
Li, Fei, Zongjun Li, Shengxiang Li, et al.. (2014). Effect of dietary physically effective fiber on ruminal fermentation and the fatty acid profile of milk in dairy goats. Journal of Dairy Science. 97(4). 2281–2290. 55 indexed citations
18.
Li, Fei, et al.. (2014). Subacute ruminal acidosis challenge changed in situ degradability of feedstuffs in dairy goats. Journal of Dairy Science. 97(8). 5101–5109. 33 indexed citations
19.
Wang, Xiaofei, Yulong Li, Xiaojun Yang, & Junhu Yao. (2013). Astragalus polysaccharide reduces inflammatory response by decreasing permeability of LPS-infected Caco2 cells. International Journal of Biological Macromolecules. 61. 347–352. 70 indexed citations
20.
Zhao, Xianghui, et al.. (2010). Effects of physically effective fiber on chewing activity, ruminal fermentation, and digestibility in goats1. Journal of Animal Science. 89(2). 501–509. 59 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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