Kai Bi

977 total citations · 1 hit paper
26 papers, 683 citations indexed

About

Kai Bi is a scholar working on Plant Science, Molecular Biology and Complementary and alternative medicine. According to data from OpenAlex, Kai Bi has authored 26 papers receiving a total of 683 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Plant Science, 6 papers in Molecular Biology and 5 papers in Complementary and alternative medicine. Recurrent topics in Kai Bi's work include Plant Disease Resistance and Genetics (12 papers), Plant-Microbe Interactions and Immunity (8 papers) and Plant and Fungal Interactions Research (4 papers). Kai Bi is often cited by papers focused on Plant Disease Resistance and Genetics (12 papers), Plant-Microbe Interactions and Immunity (8 papers) and Plant and Fungal Interactions Research (4 papers). Kai Bi collaborates with scholars based in China, Israel and United States. Kai Bi's co-authors include Amir Sharon, Tesfaye Mengiste, Dàohóng Jiāng, Ying Zhao, Yànpíng Fù, Zhixiao Gao, Jiǎtāo Xiè, Jiāsēn Chéng, Xiaohong Chen and Ronghua Dai and has published in prestigious journals such as Nature Communications, PLoS ONE and Scientific Reports.

In The Last Decade

Kai Bi

25 papers receiving 670 citations

Hit Papers

Killing softly: a roadmap of Botrytis cinerea pathogenicity 2022 2026 2023 2024 2022 50 100 150
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Killing softly: a roadmap of Botrytis cinerea pathogenicity
    2022 171 citations Kai Bi, Tesfaye Mengiste et al. Trends in Plant Science profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kai Bi China 16 465 184 96 90 72 26 683
Pratap Jyoti Handique India 12 330 0.7× 178 1.0× 19 0.2× 96 1.1× 57 0.8× 46 529
Weiwei Gao China 16 508 1.1× 241 1.3× 87 0.9× 140 1.6× 50 0.7× 53 762
Chang‐Kug Kim South Korea 16 348 0.7× 463 2.5× 61 0.6× 53 0.6× 25 0.3× 96 759
Mahendra Kumar Modi India 16 434 0.9× 334 1.8× 19 0.2× 32 0.4× 43 0.6× 74 748
Alfons Callebaut Belgium 21 1.0k 2.2× 358 1.9× 163 1.7× 130 1.4× 20 0.3× 37 1.3k
Audrey Pawtowski France 17 226 0.5× 352 1.9× 30 0.3× 85 0.9× 62 0.9× 27 915
Dominik Braun Austria 18 536 1.2× 186 1.0× 103 1.1× 47 0.5× 7 0.1× 27 894
Sofie Monbaliu Belgium 9 758 1.6× 91 0.5× 105 1.1× 134 1.5× 14 0.2× 10 868
Jae Woo Han South Korea 18 447 1.0× 265 1.4× 29 0.3× 192 2.1× 17 0.2× 43 770
Shihua Shan China 20 755 1.6× 376 2.0× 27 0.3× 81 0.9× 8 0.1× 56 909

Countries citing papers authored by Kai Bi

Since Specialization
Citations

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

Fields of papers citing papers by Kai Bi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kai Bi

This figure shows the co-authorship network connecting the top 25 collaborators of Kai Bi. A scholar is included among the top collaborators of Kai Bi 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 Kai Bi. Kai Bi 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.
Wang, Ziyao, Binbin Huang, Qiongnan Gu, et al.. (2025). The cell death-inducing protein BcPlp1 from Botrytis cinerea contributes to pathogenicity and modulates plant resistance. Plant Science. 356. 112492–112492.
2.
Zhang, Xiuli, Dandan Ma, Wenqiang Li, et al.. (2024). Correlation between optic nerve sheath diameter measured by bedside ultrasound and intracranial pressure in neurologically ill patients in a Chinese population. BMC Neurology. 24(1). 452–452. 1 indexed citations
3.
Bi, Kai, et al.. (2023). The Botrytis cinerea transglycosylase BcCrh4 is a cell death‐inducing protein with cell death‐promoting and ‐suppressing domains. Plant Cell & Environment. 47(1). 354–371. 5 indexed citations
4.
Zhu, Wenjun, Ran Xu, Dazhong Yan, et al.. (2023). Botrytis cinerea BcCDI1 protein triggers both plant cell death and immune response. Frontiers in Plant Science. 14. 1136463–1136463. 9 indexed citations
5.
Bi, Kai, et al.. (2022). Killing softly: a roadmap of Botrytis cinerea pathogenicity. Trends in Plant Science. 28(2). 211–222. 171 indexed citations breakdown →
6.
Zhu, Wenjun, Ran Xu, Kai Bi, et al.. (2022). Botrytis cinerea   BcSSP2 protein is a late infection phase, cytotoxic effector. Environmental Microbiology. 24(8). 3420–3435. 17 indexed citations
7.
Bi, Kai, Loredana Scalschi, Namrata Jaiswal, et al.. (2021). The Botrytis cinerea Crh1 transglycosylase is a cytoplasmic effector triggering plant cell death and defense response. Nature Communications. 12(1). 2166–2166. 68 indexed citations
8.
Llorens, Eugenio, et al.. (2020). Botrytis cinerea methyl isocitrate lyase mediates oxidative stress tolerance and programmed cell death by modulating cellular succinate levels. Fungal Genetics and Biology. 146. 103484–103484. 9 indexed citations
9.
Bi, Kai, Tao Chen, Zhixiao Gao, et al.. (2018). Proto-oncogenes in a eukaryotic unicellular organism play essential roles in plasmodial growth in host cells. BMC Genomics. 19(1). 881–881. 5 indexed citations
10.
Zhao, Ying, Zhixiao Gao, Binnian Tian, et al.. (2017). Endosphere microbiome comparison between symptomatic and asymptomatic roots of Brassica napus infected with Plasmodiophora brassicae. PLoS ONE. 12(10). e0185907–e0185907. 36 indexed citations
11.
Zhao, Ying, Kai Bi, Zhixiao Gao, et al.. (2017). Transcriptome Analysis of Arabidopsis thaliana in Response to Plasmodiophora brassicae during Early Infection. Frontiers in Microbiology. 8. 673–673. 47 indexed citations
12.
Bi, Kai, Zhixiao Gao, Ying Zhao, et al.. (2016). Integrated omics study of lipid droplets from Plasmodiophora brassicae. Scientific Reports. 6(1). 36965–36965. 49 indexed citations
13.
Chen, Tao, Kai Bi, Zhixiao Gao, et al.. (2016). Arabidopsis Mutant bik1 Exhibits Strong Resistance to Plasmodiophora brassicae. Frontiers in Physiology. 7. 402–402. 33 indexed citations
14.
Liu, Qing, et al.. (2014). A Universal Quantitative 1H Nuclear Magnetic Resonance (qNMR) Method for Assessing the Purity of Dammarane‐type Ginsenosides. Phytochemical Analysis. 26(1). 8–14. 23 indexed citations
15.
Sun, Li, et al.. (2012). Cytotoxic Constituents of Viscum coloratum. Zeitschrift für Naturforschung C. 67. 129–129. 7 indexed citations
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Yao, Mengqin, Yingjie Qi, Kai Bi, et al.. (2000). A Precolumn Derivatization High-Performance Liquid Chromatographic Method with Improved Sensitivity and Specificity for the Determination of Astragaloside IV in Radix Astragali. Journal of Chromatographic Science. 38(8). 325–328. 17 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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