Lupei Zhang

3.4k total citations
119 papers, 2.3k citations indexed

About

Lupei Zhang is a scholar working on Genetics, Cancer Research and Molecular Biology. According to data from OpenAlex, Lupei Zhang has authored 119 papers receiving a total of 2.3k indexed citations (citations by other indexed papers that have themselves been cited), including 98 papers in Genetics, 63 papers in Cancer Research and 33 papers in Molecular Biology. Recurrent topics in Lupei Zhang's work include Genetic and phenotypic traits in livestock (87 papers), Genetic Mapping and Diversity in Plants and Animals (74 papers) and Cancer-related molecular mechanisms research (59 papers). Lupei Zhang is often cited by papers focused on Genetic and phenotypic traits in livestock (87 papers), Genetic Mapping and Diversity in Plants and Animals (74 papers) and Cancer-related molecular mechanisms research (59 papers). Lupei Zhang collaborates with scholars based in China, United States and Belgium. Lupei Zhang's co-authors include Junya Li, Huijiang Gao, Xue Gao, Lingyang Xu, Yan Chen, Bo Zhu, Tianpeng Chang, Zezhao Wang, ‬Min Du and Bo Zhu and has published in prestigious journals such as PLoS ONE, Cell Metabolism and Scientific Reports.

In The Last Decade

Lupei Zhang

117 papers receiving 2.2k citations

Peers

Lupei Zhang

GENET

ASZ

ACS

Linsen Zan China

Xianyong Lan China

Henry Reyer Germany

Linsen Zan China

Honglin Jiang United States

Akiko Takasuga Japan

Aline Silva Mello César Brazil

Myunggi Baik South Korea

Mei Yu China

Linsen Zan China

Lupei Zhang

1.0k ×0.7

GENET

894 ×1.4

1.3k ×2.3

450 ×1.2

ASZ

418 ×1.4

ACS

Citations per year, relative to Lupei Zhang Lupei Zhang (= 1×) peers Linsen Zan

Countries citing papers authored by Lupei Zhang

Since Specialization

Citations

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

Fields of papers citing papers by Lupei Zhang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lupei Zhang

This figure shows the co-authorship network connecting the top 25 collaborators of Lupei Zhang. A scholar is included among the top collaborators of Lupei Zhang 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 Lupei Zhang. Lupei Zhang 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

Wu, Tianyi, Tianliu Zhang, Zheng Xu, et al.. (2025). Comprehensive Multi-omics Analysis of Regulatory Variants for Body Weight in Cattle. Genomics Proteomics & Bioinformatics. 23(4).

Wang, Zezhao, Lupei Zhang, Huijiang Gao, et al.. (2024). Integrating multi-layered biological priors to improve genomic prediction accuracy in beef cattle. Biology Direct. 19(1). 147–147. 1 indexed citations

Zhang, Yapeng, Wentao Cai, Qi Zhang, et al.. (2024). Integrated analyses of genomic and transcriptomic data reveal candidate variants associated with carcass traits in Huaxi cattle 1. Journal of Integrative Agriculture. 1 indexed citations

Wang, Yuanqing, Jing Wang, Lupei Zhang, et al.. (2024). Genome-Wide Detection of Copy Number Variations and Their Potential Association with Carcass and Meat Quality Traits in Pingliang Red Cattle. International Journal of Molecular Sciences. 25(11). 5626–5626. 4 indexed citations

Li, Hongwei, Bo Zhu, Lupei Zhang, et al.. (2024). Improving Genomic Predictions in Multi-Breed Cattle Populations: A Comparative Analysis of BayesR and GBLUP Models. Genes. 15(2). 253–253. 3 indexed citations

Yuan, Feng, Yu Wang, Juan Zhang, et al.. (2024). Genome-Wide Scans for Selection Signatures in Ningxia Angus Cattle Reveal Genetic Variants Associated with Economic and Adaptive Traits. Animals. 15(1). 58–58. 1 indexed citations

Wang, Yuanqing, Jing Wang, Lupei Zhang, et al.. (2023). Genetic Origin and Introgression Pattern of Pingliang Red Cattle Revealed Using Genome-Wide SNP Analyses. Genes. 14(12). 2198–2198.

Liang, Mang, Bingxing An, Tianyu Deng, et al.. (2023). Incorporating genome-wide and transcriptome-wide association studies to identify genetic elements of longissimus dorsi muscle in Huaxi cattle. Frontiers in Genetics. 13. 982433–982433. 6 indexed citations

Wang, Jing, Tingting Fan, Lingyang Xu, et al.. (2023). Genome-Wide Association Analysis Identifies the PMEL Gene Affecting Coat Color and Birth Weight in Simmental × Holstein. Animals. 13(24). 3821–3821. 7 indexed citations

10.

Bordbar, Farhad, Mohammadreza Mohammadabadi, Just Jensen, et al.. (2022). Identification of Candidate Genes Regulating Carcass Depth and Hind Leg Circumference in Simmental Beef Cattle Using Illumina Bovine Beadchip and Next-Generation Sequencing Analyses. Animals. 12(9). 1103–1103. 30 indexed citations

11.

Li, Hongwei, Zezhao Wang, Lei Xu, et al.. (2022). Genomic prediction of carcass traits using different haplotype block partitioning methods in beef cattle. Evolutionary Applications. 15(12). 2028–2042. 5 indexed citations

12.

Liu, Li, Peili Cao, Lupei Zhang, et al.. (2021). Comparisons of adipogenesis- and lipid metabolism-related gene expression levels in muscle, adipose tissue and liver from Wagyu-cross and Holstein steers. PLoS ONE. 16(2). e0247559–e0247559. 17 indexed citations

13.

Li, Hongwei, Bo Zhu, Zezhao Wang, et al.. (2021). Genomic Prediction Using LD-Based Haplotypes Inferred From High-Density Chip and Imputed Sequence Variants in Chinese Simmental Beef Cattle. Frontiers in Genetics. 12. 665382–665382. 14 indexed citations

14.

Hu, Xin, Ling Ren, Yahui Wang, et al.. (2020). bta-miR-23a Regulates the Myogenic Differentiation of Fetal Bovine Skeletal Muscle-Derived Progenitor Cells by Targeting MDFIC Gene. Genes. 11(10). 1232–1232. 8 indexed citations

15.

Bordbar, Farhad, Just Jensen, ‬Min Du, et al.. (2020). Identification and validation of a novel candidate gene regulating net meat weight in Simmental beef cattle based on imputed next‐generation sequencing. Cell Proliferation. 53(9). e12870–e12870. 10 indexed citations

16.

Hu, Xin, et al.. (2018). Identification of microRNAs involved in myogenic differentiation of bovine fetal skeletal muscle derived myoblasts.. 49(6). 1134–1144. 1 indexed citations

17.

Xu, Lingyang, Yan Chen, Lupei Zhang, et al.. (2016). Genome-Wide Association Study Reveals the PLAG1 Gene for Knuckle, Biceps and Shank Weight in Simmental Beef Cattle. PLoS ONE. 11(12). e0168316–e0168316. 36 indexed citations

18.

Wu, Yang, Wengang Zhang, Jiangwei Xia, et al.. (2016). Estimation of Genetic Parameters for Economic Important Traits in Chinese Simmental Beef Cattle. 47(9). 1823. 1 indexed citations

19.

Chen, Cui, Zhengrong Yuan, Xiaojie Chen, et al.. (2013). Genetic polymorphisms of Mc4R and IGF2 gene association with feed conversion efficiency traits in beef cattle.. Pakistan Veterinary Journal. 33(4). 418–422. 8 indexed citations

20.

Zhang, Lupei, Shang‐Zhong Xu, Hengde Li, et al.. (2008). Association of LXRA gene variants with carcass and meat quality traits in beef cattle. AFRICAN JOURNAL OF BIOTECHNOLOGY. 7(20). 3546–3549. 3 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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