Yuling Bai

10.3k total citations
127 papers, 4.8k citations indexed

About

Yuling Bai is a scholar working on Plant Science, Molecular Biology and Cell Biology. According to data from OpenAlex, Yuling Bai has authored 127 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 119 papers in Plant Science, 20 papers in Molecular Biology and 14 papers in Cell Biology. Recurrent topics in Yuling Bai's work include Plant-Microbe Interactions and Immunity (65 papers), Plant Pathogens and Resistance (50 papers) and Plant Virus Research Studies (35 papers). Yuling Bai is often cited by papers focused on Plant-Microbe Interactions and Immunity (65 papers), Plant Pathogens and Resistance (50 papers) and Plant Virus Research Studies (35 papers). Yuling Bai collaborates with scholars based in Netherlands, China and Italy. Yuling Bai's co-authors include Richard G. F. Visser, Stefano Pavan, Anne‐Marie A. Wolters, E. Jacobsen, Michela Appiano, Christos Kissoudis, P. Lindhout, Richard Kormelink, Henk J. Schouten and Zhe Yan and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

Yuling Bai

122 papers receiving 4.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yuling Bai Netherlands 40 4.5k 1.4k 509 443 383 127 4.8k
Bingyan Xie China 33 2.6k 0.6× 1000 0.7× 460 0.9× 407 0.9× 530 1.4× 122 3.3k
Amir Sherman Israel 32 2.3k 0.5× 1.7k 1.2× 605 1.2× 235 0.5× 387 1.0× 71 3.3k
Byoung‐Cheorl Kang South Korea 37 3.7k 0.8× 1.6k 1.2× 125 0.2× 265 0.6× 449 1.2× 162 4.3k
Guido Van den Ackerveken Netherlands 37 4.6k 1.0× 1.3k 1.0× 915 1.8× 236 0.5× 281 0.7× 78 5.1k
Dingzhong Tang China 38 5.4k 1.2× 2.1k 1.5× 479 0.9× 250 0.6× 283 0.7× 109 5.9k
Yuese Ning China 35 3.3k 0.7× 1.8k 1.3× 524 1.0× 214 0.5× 307 0.8× 73 3.9k
Belén Picó Spain 38 3.6k 0.8× 1.1k 0.8× 244 0.5× 392 0.9× 1.7k 4.4× 152 4.3k
Tesfaye Mengiste United States 42 6.7k 1.5× 3.0k 2.2× 806 1.6× 350 0.8× 249 0.7× 75 7.4k
Takehiko Shimada Japan 32 2.4k 0.5× 1.9k 1.4× 236 0.5× 136 0.3× 163 0.4× 113 3.3k
Rosa Figueroa‐Balderas United States 20 1.7k 0.4× 1.4k 1.0× 311 0.6× 132 0.3× 353 0.9× 38 2.5k

Countries citing papers authored by Yuling Bai

Since Specialization
Citations

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

Fields of papers citing papers by Yuling Bai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuling Bai

This figure shows the co-authorship network connecting the top 25 collaborators of Yuling Bai. A scholar is included among the top collaborators of Yuling Bai 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 Yuling Bai. Yuling Bai 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.
Lin, Runmao, Jian Ling, Zhenchuan Mao, et al.. (2025). Comparative transcriptomics of susceptible and resistant Cucumis metuliferus upon Meloidogyne incognita infection. Planta. 261(4). 72–72.
2.
Winter, David J., Kazuya Maeda, Yuichiro Iida, et al.. (2024). Sequential breakdown of the Cf‐9 leaf mould resistance locus in tomato by Fulvia fulva. New Phytologist. 243(4). 1522–1538. 5 indexed citations
3.
Fuentes, Roven Rommel, Thamara Hesselink, W. van Dooijeweert, et al.. (2024). A catalogue of recombination coldspots in interspecific tomato hybrids. PLoS Genetics. 20(7). e1011336–e1011336. 2 indexed citations
4.
Valentino, Danila, Silvia Gianoglio, Yuling Bai, et al.. (2024). Knock-out of SlDMR6-1 in tomato promotes a drought-avoidance strategy and increases tolerance to Late Blight. Plant Stress. 13. 100541–100541. 4 indexed citations
5.
Ling, Jian, Zhenchuan Mao, Jianlong Zhao, et al.. (2024). Genetic dissection of Meloidogyne incognita resistance genes based on VIGS functional analysis in Cucumis metuliferus. BMC Plant Biology. 24(1). 964–964. 4 indexed citations
6.
Bracuto, Valentina, Fien Meijer‐Dekens, Anne‐Marie A. Wolters, et al.. (2024). Less is more: CRISPR/Cas9-based mutations in DND1 gene enhance tomato resistance to powdery mildew with low fitness costs. BMC Plant Biology. 24(1). 763–763. 4 indexed citations
7.
Lanteri, Sergio, et al.. (2023). Genomic Analysis Highlights Putative Defective Susceptibility Genes in Tomato Germplasm. Plants. 12(12). 2289–2289. 2 indexed citations
8.
Cui, Lei, et al.. (2023). Resistance to Anthracnose Rot Disease in Capsicum. Agronomy. 13(5). 1434–1434. 6 indexed citations
9.
Kohlen, Wouter, et al.. (2023). Inactivation of tomato WAT1 leads to reduced susceptibility to Clavibacter michiganensis through downregulation of bacterial virulence factors. Frontiers in Plant Science. 14. 1082094–1082094. 7 indexed citations
10.
Gao, Dongli, Michela Appiano, Robin P. Huibers, et al.. (2023). ZED1-related kinase 13 is required for resistance against Pseudoidium neolycopersici in Arabidopsis accession Bla-6. Frontiers in Plant Science. 14. 1111322–1111322. 1 indexed citations
11.
Mao, Jingjing, Haiying Xiang, Wanli Zeng, et al.. (2023). Genome-wide identification of CBL family genes in Nicotiana tabacum and the functional analysis of NtCBL4A-1 under salt stress. Environmental and Experimental Botany. 209. 105311–105311. 2 indexed citations
12.
Yan, Zhe, Yuling Bai, Danila Valentino, et al.. (2022). CRISPR/Cas9-Based Knock-Out of the PMR4 Gene Reduces Susceptibility to Late Blight in Two Tomato Cultivars. International Journal of Molecular Sciences. 23(23). 14542–14542. 17 indexed citations
13.
Arens, Paul, Xintong Liu, Xin Zhang, et al.. (2021). Analysis of allelic variants of RhMLO genes in rose and functional studies on susceptibility to powdery mildew related to clade V homologs. Theoretical and Applied Genetics. 134(8). 2495–2515. 5 indexed citations
14.
Luo, Quan‐Yong, et al.. (2020). Assessment Causality in Associations Between Serum Uric Acid and Risk of Schizophrenia: A Two-Sample Bidirectional Mendelian Randomization Study. SHILAP Revista de lepidopterología. 2 indexed citations
15.
Bracuto, Valentina, Michela Appiano, E. Jacobsen, et al.. (2020). CRISPR/Cas9-targeted mutagenesis of the tomato susceptibility gene PMR4 for resistance against powdery mildew. BMC Plant Biology. 20(1). 284–284. 118 indexed citations
16.
Schouten, Henk J., Yury Tikunov, W. Verkerke, et al.. (2019). Breeding Has Increased the Diversity of Cultivated Tomato in The Netherlands. Frontiers in Plant Science. 10. 1606–1606. 83 indexed citations
17.
Seifi, Alireza, Richard G. F. Visser, & Yuling Bai. (2011). DIFFERENTIAL EXPRESSION OF TIR-LIKE GENES EMBEDDED IN THE MI-1 GENE CLUSTER IN NEMATODE-RESISTANT AND –SUSCEPTIBLE TOMATO ROOTS. Journal of Plant Pathology. 93(3). 701–706. 2 indexed citations
18.
Vossen, Jack H., Ahmed Abd‐El‐Haliem, Emilie F. Fradin, et al.. (2010). Identification of tomato phosphatidylinositol-specific phospholipase-C (PI-PLC) family members and the role of PLC4 and PLC6 in HR and disease resistance. The Plant Journal. 62(2). 224–239. 130 indexed citations
19.
Bai, Yuling, et al.. (2006). Molecular markers and their use in genetic studies in rose.. 498–503. 2 indexed citations
20.

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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