Xiping Wang

11.1k total citations
191 papers, 7.4k citations indexed

About

Xiping Wang is a scholar working on Plant Science, Molecular Biology and Food Science. According to data from OpenAlex, Xiping Wang has authored 191 papers receiving a total of 7.4k indexed citations (citations by other indexed papers that have themselves been cited), including 146 papers in Plant Science, 111 papers in Molecular Biology and 22 papers in Food Science. Recurrent topics in Xiping Wang's work include Plant Gene Expression Analysis (67 papers), Horticultural and Viticultural Research (59 papers) and Plant Molecular Biology Research (36 papers). Xiping Wang is often cited by papers focused on Plant Gene Expression Analysis (67 papers), Horticultural and Viticultural Research (59 papers) and Plant Molecular Biology Research (36 papers). Xiping Wang collaborates with scholars based in China, United States and Tunisia. Xiping Wang's co-authors include Citao Liu, Min Gao, Zhi Li, Yuejin Wang, Haowen Li, Chunlei Guo, Yanbin Wu, Xianhang Wang, Stacy D. Singer and Xing‐Wang Deng and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Xiping Wang

187 papers receiving 7.3k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Xiping Wang China 46 5.5k 4.2k 496 435 383 191 7.4k
Xing Zhang China 39 2.5k 0.5× 3.6k 0.9× 314 0.6× 382 0.9× 451 1.2× 285 6.6k
Margaret H. Frank United States 15 7.2k 1.3× 6.3k 1.5× 397 0.8× 640 1.5× 385 1.0× 29 10.4k
Hannah Rae Thomas China 6 6.5k 1.2× 6.0k 1.4× 295 0.6× 602 1.4× 343 0.9× 15 9.6k
Chen Jiao China 35 2.8k 0.5× 1.6k 0.4× 237 0.5× 451 1.0× 180 0.5× 113 4.2k
Mukesh Jain India 52 8.6k 1.6× 6.4k 1.5× 261 0.5× 1.3k 3.0× 277 0.7× 147 12.2k
Ursula Kües Germany 39 3.3k 0.6× 2.0k 0.5× 629 1.3× 236 0.5× 178 0.5× 118 4.9k
Éva Kondorosi Hungary 66 10.1k 1.8× 3.2k 0.8× 385 0.8× 244 0.6× 179 0.5× 180 11.9k
Lourdes Gómez‐Gómez Spain 38 6.8k 1.2× 4.1k 1.0× 387 0.8× 158 0.4× 501 1.3× 108 9.7k
Chun‐Xiang You China 49 7.3k 1.3× 6.1k 1.5× 254 0.5× 113 0.3× 259 0.7× 237 9.4k

Countries citing papers authored by Xiping Wang

Since Specialization
Citations

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

Fields of papers citing papers by Xiping Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Xiping Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Xiping Wang. A scholar is included among the top collaborators of Xiping Wang 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 Xiping Wang. Xiping Wang 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.
Li, Zhi, et al.. (2025). Plant–pathogen interactions and ambient pH dynamics. Stress Biology. 5(1). 5 indexed citations
2.
Zhang, Qihan, Yanxun Zhu, Yichu Zhang, et al.. (2025). A module with multiple transcription factors positively regulates powdery mildew resistance in grapevine. Plant Biotechnology Journal. 23(9). 3984–3999. 1 indexed citations
3.
Wang, Xiping, et al.. (2024). GeoVis: a data-driven geographic visualization recommendation system via latent space encoding. Journal of Visualization. 27(4). 603–622.
4.
Yao, Jin, Songlin Zhang, Na Wu, et al.. (2023). KNOX transcription factor VvHB63 affects grape seed development by interacting with protein VvHB06. Plant Science. 330. 111665–111665. 4 indexed citations
5.
Zhong, Haixia, Vivek Yadav, Chuan Zhang, et al.. (2023). The Impact of High Temperatures in the Field on Leaf Tissue Structure in Different Grape Cultivars. Horticulturae. 9(7). 731–731. 9 indexed citations
6.
Yao, Jin, Xingmei Li, Na Wu, et al.. (2023). Improvement of RNA In Situ Hybridisation for Grapevine Fruits and Ovules. International Journal of Molecular Sciences. 24(1). 800–800. 2 indexed citations
7.
Ma, Hai, Dongmei Li, Yiping Zhang, et al.. (2023). Transcription Factor MdbHLH093 Enhances Powdery Mildew Resistance by Promoting Salicylic Acid Signaling and Hydrogen Peroxide Accumulation. International Journal of Molecular Sciences. 24(11). 9390–9390. 10 indexed citations
8.
Zhang, Songlin, Jin Yao, Lı Wang, et al.. (2022). Role of grapevine SEPALLATA‐related MADS‐box gene VvMADS39 in flower and ovule development. The Plant Journal. 111(6). 1565–1579. 23 indexed citations
9.
Zhang, Songlin, et al.. (2021). NAC domain gene VvNAC26 interacts with VvMADS9 and influences seed and fruit development. Plant Physiology and Biochemistry. 164. 63–72. 27 indexed citations
10.
Li, Zhi, et al.. (2021). The Impact of Elsinoë ampelina Infection on Key Metabolic Properties in Vitis vinifera ‘Red Globe’ Berries via Multiomics Approaches. Molecular Plant-Microbe Interactions. 35(1). 15–27. 11 indexed citations
11.
Zhang, Songlin, Xingmei Li, Xiuming Zhang, et al.. (2020). A MADS-box transcription factor from grapevine, VvMADS45, influences seed development. Plant Cell Tissue and Organ Culture (PCTOC). 141(1). 105–118. 15 indexed citations
12.
Ahmad, Bilal, Jin Yao, Songlin Zhang, et al.. (2020). Genome-Wide Characterization and Expression Profiling of GASA Genes during Different Stages of Seed Development in Grapevine (Vitis vinifera L.) Predict Their Involvement in Seed Development. International Journal of Molecular Sciences. 21(3). 1088–1088. 46 indexed citations
13.
Gao, Min, Yanxun Zhu, Jinhua Yang, et al.. (2019). Identification of the grape basic helix–loop–helix transcription factor family and characterization of expression patterns in response to different stresses. Plant Growth Regulation. 88(1). 19–39. 17 indexed citations
14.
Xu, Weirong, Fuli Ma, Ruimin Li, et al.. (2019). VpSTS29/STS2 enhances fungal tolerance in grapevine through a positive feedback loop. Plant Cell & Environment. 42(11). 2979–2998. 27 indexed citations
15.
Li, Zhi, et al.. (2019). The Endophytic Fungus Albifimbria verrucaria from Wild Grape as an Antagonist of Botrytis cinerea and Other Grape Pathogens. Phytopathology. 110(4). 843–850. 24 indexed citations
16.
Hanif, Muhammad, Mati Ur Rahman, Min Gao, et al.. (2018). Heterologous Expression of the Grapevine JAZ7 Gene in Arabidopsis Confers Enhanced Resistance to Powdery Mildew but Not to Botrytis cinerea. International Journal of Molecular Sciences. 19(12). 3889–3889. 6 indexed citations
17.
18.
Yang, Jinhua, Min Gao, Li Huang, et al.. (2017). Identification and expression analysis of the apple (Malus × domestica) basic helix-loop-helix transcription factor family. Scientific Reports. 7(1). 28–28. 46 indexed citations
19.
Wang, Xiping, et al.. (2010). Isolation and sequence analysis of calmodulin gene of Chinese Wild Vitis quinquangularis.. Xibei zhiwu xuebao. 30(8). 1507–1513. 1 indexed citations
20.
Zhang, Yuan, Baoguo Li, Yan Chen, et al.. (2007). Monte Carlo simulation of solar radiation in maize canopies and its visualisation. New Zealand Journal of Agricultural Research. 50(5). 553–558. 1 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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