Chaoliang Wen

1.5k total citations
36 papers, 771 citations indexed

About

Chaoliang Wen is a scholar working on Animal Science and Zoology, Molecular Biology and Genetics. According to data from OpenAlex, Chaoliang Wen has authored 36 papers receiving a total of 771 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Animal Science and Zoology, 21 papers in Molecular Biology and 14 papers in Genetics. Recurrent topics in Chaoliang Wen's work include Animal Nutrition and Physiology (21 papers), Gut microbiota and health (15 papers) and Genetic and phenotypic traits in livestock (10 papers). Chaoliang Wen is often cited by papers focused on Animal Nutrition and Physiology (21 papers), Gut microbiota and health (15 papers) and Genetic and phenotypic traits in livestock (10 papers). Chaoliang Wen collaborates with scholars based in China and Indonesia. Chaoliang Wen's co-authors include Ning Yang, Wei Yan, Jiangxia Zheng, Congjiao Sun, Congliang Ji, Dexiang Zhang, Qianqian Zhou, Congjiao Sun, Fangren Lan and Zhongyi Duan and has published in prestigious journals such as International Journal of Molecular Sciences, Applied Microbiology and Biotechnology and Frontiers in Microbiology.

In The Last Decade

Chaoliang Wen

32 papers receiving 766 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Chaoliang Wen China	14	426	402	167	111	90	36	771
Dirkjan Schokker Netherlands	15	432 1.0×	406 1.0×	78 0.5×	207 1.9×	183 2.0×	62	885
Zhengxiao Zhai China	7	544 1.3×	282 0.7×	67 0.4×	212 1.9×	190 2.1×	7	748
Jianmin Zou China	17	178 0.4×	458 1.1×	124 0.7×	105 0.9×	36 0.4×	50	824
Yaxiong Jia China	15	164 0.4×	279 0.7×	225 1.3×	65 0.6×	84 0.9×	37	614
Tejas M. Shah India	12	296 0.7×	211 0.5×	105 0.6×	104 0.9×	33 0.4×	23	554
Francesc Molist Netherlands	15	279 0.7×	677 1.7×	64 0.4×	208 1.9×	84 0.9×	52	1.1k
Xiang Ma China	11	355 0.8×	119 0.3×	85 0.5×	109 1.0×	94 1.0×	51	651
Ronghua Dai China	10	298 0.7×	158 0.4×	72 0.4×	77 0.7×	85 0.9×	11	478
Kellie Watson United Kingdom	13	192 0.5×	366 0.9×	150 0.9×	107 1.0×	77 0.9×	28	612
D. N. Rank India	13	357 0.8×	285 0.7×	199 1.2×	234 2.1×	42 0.5×	70	813

Countries citing papers authored by Chaoliang Wen

Since Specialization

Citations

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

Fields of papers citing papers by Chaoliang Wen

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chaoliang Wen

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

All Works

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20 of 20 papers shown

Lan, Fangren, Qianqian Zhou, Xiaochang Li, et al.. (2025). Deciphering the coordinated roles of the host genome, duodenal mucosal genes, and microbiota in regulating complex traits in chickens. Microbiome. 13(1). 62–62.

Gu, Shuang, Qiang Huang, Junying Li, et al.. (2024). Precise quantification of skeletal muscle fibers reveals the physiological basis for growth rate discrepancies in broilers1. Journal of Integrative Agriculture. 1 indexed citations

Yan, Yiyuan, et al.. (2024). Pedigree reconstruction based on genotype data in chickens. Poultry Science. 103(12). 104327–104327.

Sun, Congjiao, Fangren Lan, Chaoliang Wen, et al.. (2024). Mechanisms of hepatic steatosis in chickens: integrated analysis of the host genome, molecular phenomics and gut microbiome. GigaScience. 13. 5 indexed citations

Gu, Shuang, Qiang Huang, Congjiao Sun, Chaoliang Wen, & Ning Yang. (2024). Transcriptomic and epigenomic insights into pectoral muscle fiber formation at the late embryonic development in pure chicken lines. Poultry Science. 103(8). 103882–103882. 5 indexed citations

Dai, Dongbo, Fangren Lan, Chaoliang Wen, et al.. (2024). Proteomic and N-glycosylation analysis of fertile egg white during storage and incubation in chickens. Poultry Science. 104(1). 104526–104526.

Wang, Bin, et al.. (2024). Paternity bias and cryptic female choice in chickens. Poultry Science. 103(6). 103744–103744. 1 indexed citations

Li, Quan‐Lin, Xiaochang Li, Fangren Lan, et al.. (2024). Calcium deposition in chicken eggshells: role of host genetics and gut microbiota. Poultry Science. 103(10). 104073–104073. 2 indexed citations

Wen, Chaoliang, Qiang Huang, Jia Gao, et al.. (2024). MyoV: a deep learning-based tool for the automated quantification of muscle fibers. Briefings in Bioinformatics. 25(2). 7 indexed citations

10.

Wen, Chaoliang, et al.. (2023). Emerging perspectives in the gut–muscle axis: The gut microbiota and its metabolites as important modulators of meat quality. Microbial Biotechnology. 17(1). e14361–e14361. 13 indexed citations

11.

Guo, Xiaoli, Qianqian Zhou, Fangren Lan, et al.. (2023). Hepatic steatosis is associated with dysregulated cholesterol metabolism and altered protein acetylation dynamics in chickens. Journal of Animal Science and Biotechnology. 14(1). 108–108. 3 indexed citations

12.

Jiang, Xinwei, Fangren Lan, Xiaochang Li, et al.. (2023). Host genetics and gut microbiota jointly regulate blood biochemical indicators in chickens. Applied Microbiology and Biotechnology. 107(24). 7601–7620. 13 indexed citations

13.

Wen, Chaoliang, et al.. (2023). The cecal ecosystem is a great contributor to intramuscular fat deposition in broilers. Poultry Science. 102(4). 102568–102568. 23 indexed citations

14.

Wen, Chaoliang, et al.. (2022). Inheritance of the duration of fertility in chickens and its correlation with laying performance. Genetics Selection Evolution. 54(1). 41–41. 12 indexed citations

15.

Zhou, Qianqian, Fangren Lan, Guangqi Li, et al.. (2022). Genetic and microbiome analysis of feed efficiency in laying hens. Poultry Science. 102(4). 102393–102393. 13 indexed citations

16.

Zhou, Qianqian, Fangren Lan, Xiaochang Li, et al.. (2021). The Spatial and Temporal Characterization of Gut Microbiota in Broilers. Frontiers in Veterinary Science. 8. 712226–712226. 46 indexed citations

17.

Wen, Chaoliang, Wei Yan, Zhongyi Duan, et al.. (2021). Joint contributions of the gut microbiota and host genetics to feed efficiency in chickens. Microbiome. 9(1). 126–126. 100 indexed citations

18.

Wen, Chaoliang, et al.. (2020). Detrimental effects of excessive fatty acid secretion on female sperm storage in chickens. Journal of Animal Science and Biotechnology. 11(1). 26–26. 23 indexed citations

19.

Yan, Wei, Congjiao Sun, Jiangxia Zheng, et al.. (2019). Efficacy of Fecal Sampling as a Gut Proxy in the Study of Chicken Gut Microbiota. Frontiers in Microbiology. 10. 2126–2126. 96 indexed citations

20.

Wen, Chaoliang, Wei Yan, Jiangxia Zheng, et al.. (2018). Feed efficiency measures and their relationships with production and meat quality traits in slower growing broilers. Poultry Science. 97(7). 2356–2364. 63 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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