Pingxian Wu

468 total citations
32 papers, 283 citations indexed

About

Pingxian Wu is a scholar working on Genetics, Cancer Research and Molecular Biology. According to data from OpenAlex, Pingxian Wu has authored 32 papers receiving a total of 283 indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Genetics, 16 papers in Cancer Research and 8 papers in Molecular Biology. Recurrent topics in Pingxian Wu's work include Genetic and phenotypic traits in livestock (23 papers), Genetic Mapping and Diversity in Plants and Animals (22 papers) and Cancer-related molecular mechanisms research (16 papers). Pingxian Wu is often cited by papers focused on Genetic and phenotypic traits in livestock (23 papers), Genetic Mapping and Diversity in Plants and Animals (22 papers) and Cancer-related molecular mechanisms research (16 papers). Pingxian Wu collaborates with scholars based in China and India. Pingxian Wu's co-authors include Kai Wang, Guoqing Tang, Yanzhi Jiang, Anan Jiang, Jie Zhou, Weihang Xiao, Xuewei Li, Li Zhu, Qiang Yang and Jideng Ma and has published in prestigious journals such as Scientific Reports, International Journal of Molecular Sciences and Meat Science.

In The Last Decade

Pingxian Wu

31 papers receiving 281 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Pingxian Wu China 11 205 98 63 61 24 32 283
Richard G. Sherlock New Zealand 7 218 1.1× 61 0.6× 51 0.8× 89 1.5× 12 0.5× 8 287
Weihang Xiao China 11 186 0.9× 130 1.3× 106 1.7× 60 1.0× 16 0.7× 20 332
Gordon Vander Voort Canada 10 267 1.3× 66 0.7× 28 0.4× 93 1.5× 12 0.5× 13 322
Abulgasim Ahbara United Kingdom 8 222 1.1× 91 0.9× 35 0.6× 61 1.0× 19 0.8× 17 266
Shuqi Diao China 11 160 0.8× 74 0.8× 50 0.8× 53 0.9× 15 0.6× 21 213
Zitao Chen China 13 296 1.4× 127 1.3× 85 1.3× 79 1.3× 37 1.5× 32 391
Shaoxiong Lu China 8 173 0.8× 97 1.0× 51 0.8× 43 0.7× 8 0.3× 26 234
B. Fan China 10 380 1.9× 81 0.8× 87 1.4× 103 1.7× 32 1.3× 17 447
Lijin Guo China 10 94 0.5× 170 1.7× 210 3.3× 108 1.8× 9 0.4× 23 358
Jinyan Teng China 12 205 1.0× 69 0.7× 51 0.8× 68 1.1× 18 0.8× 27 268

Countries citing papers authored by Pingxian Wu

Since Specialization
Citations

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

Fields of papers citing papers by Pingxian Wu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pingxian Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Pingxian Wu. A scholar is included among the top collaborators of Pingxian Wu 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 Pingxian Wu. Pingxian Wu 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.
Wu, Pingxian, et al.. (2025). Optimization of genomic breeding value prediction for growth traits in Rongchang pigs through machine learning techniques. Machine Learning with Applications. 22. 100747–100747. 1 indexed citations
2.
Chen, Ziyu, Xiaoqian Wu, Yu‐Ling Wang, et al.. (2025). Single-Nucleus RNA Sequencing Reveals Cellular Transcriptome Features at Different Growth Stages in Porcine Skeletal Muscle. Cells. 14(1). 37–37. 3 indexed citations
3.
Chen, Li, Xi Long, Shuqi Diao, et al.. (2025). Enhancing Genomic Prediction Accuracy of Reproduction Traits in Rongchang Pigs Through Machine Learning. Animals. 15(4). 525–525. 2 indexed citations
4.
Chao, Anne, Bin Zhang, Zhi Tu, et al.. (2024). Multiomics Reveals the Microbiota and Metabolites Associated with Sperm Quality in Rongchang Boars. Microorganisms. 12(6). 1077–1077. 1 indexed citations
5.
Wang, Shujie, Dong Chen, Xiang Ji, et al.. (2024). Multi-omics unveils tryptophan metabolic pathway as a key pathway influencing residual feed intake in Duroc swine. Frontiers in Veterinary Science. 11. 1403493–1403493. 1 indexed citations
6.
Chen, Dong, Xiang Ji, Qi Shen, et al.. (2023). Genomic prediction of pig growth traits based on machine learning.. PubMed. 45(10). 922–932. 1 indexed citations
7.
Wang, Kai, Shujie Wang, Xiang Ji, et al.. (2023). Epigenome-wide association studies of meat traits in Chinese Yorkshire pigs highlights several DNA methylation loci and genes. Frontiers in Genetics. 13. 1028711–1028711. 3 indexed citations
8.
Wu, Pingxian, Xiang Ji, Li Chen, et al.. (2023). CYP24A1 is associated with fetal mummification in pigs. Theriogenology. 211. 105–114. 1 indexed citations
9.
Wu, Pingxian, Kai Wang, Shujie Wang, et al.. (2022). Whole-genome sequence association study identifies cyclin dependent kinase 8 as a key gene for the number of mummified piglets. Animal Bioscience. 36(1). 29–42.
10.
Wang, Shujie, Pingxian Wu, Kai Wang, et al.. (2022). Comparative metabolome profiling of serum and urine from sows with a high prevalence of piglet mummification and normal sows at different stages of pregnancy. Theriogenology. 183. 10–25. 3 indexed citations
11.
Wu, Pingxian, Kai Wang, Jie Zhou, et al.. (2021). A combined GWAS approach reveals key loci for socially-affected traits in Yorkshire pigs. Communications Biology. 4(1). 891–891. 13 indexed citations
12.
Chen, Dong, Pingxian Wu, Kai Wang, et al.. (2021). Combining computer vision score and conventional meat quality traits to estimate the intramuscular fat content using machine learning in pigs. Meat Science. 185. 108727–108727. 24 indexed citations
13.
Wang, Kai, Pingxian Wu, Shujie Wang, et al.. (2021). Differential DNA methylation analysis reveals key genes in Chinese Qingyu and Landrace pigs. Genome. 65(1). 19–26. 2 indexed citations
14.
Wang, Kai, Pingxian Wu, Jie Zhou, et al.. (2021). Detecting the selection signatures in Chinese Duroc,Landrace, Yorkshire, Liangshan, and Qingyu pigs. Functional & Integrative Genomics. 21(5-6). 655–664. 9 indexed citations
15.
Wu, Pingxian, Kai Wang, Jie Zhou, et al.. (2019). GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs. Frontiers in Genetics. 10. 1012–1012. 22 indexed citations
16.
Wu, Pingxian, Kai Wang, Qiang Yang, et al.. (2019). Whole-genome re-sequencing association study for direct genetic effects and social genetic effects of six growth traits in Large White pigs. Scientific Reports. 9(1). 9667–9667. 14 indexed citations
17.
Wu, Pingxian, Kai Wang, Jie Zhou, et al.. (2019). A genome wide association study for the number of animals born dead in domestic pigs. BMC Genetics. 20(1). 4–4. 12 indexed citations
18.
Yang, Qiang, Pingxian Wu, Kai Wang, et al.. (2018). SNPs associated with body weight and backfat thickness in two pig breeds identified by a genome-wide association study. Genomics. 111(6). 1583–1589. 18 indexed citations
19.
Wang, Kai, Pingxian Wu, Qiang Yang, et al.. (2018). Detection of Selection Signatures in Chinese Landrace and Yorkshire Pigs Based on Genotyping-by-Sequencing Data. Frontiers in Genetics. 9. 119–119. 38 indexed citations
20.
Wu, Pingxian, Qiang Yang, Kai Wang, et al.. (2017). Single step genome-wide association studies based on genotyping by sequence data reveals novel loci for the litter traits of domestic pigs. Genomics. 110(3). 171–179. 45 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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