Kun Feng

1.6k total citations
49 papers, 1.2k citations indexed

About

Kun Feng is a scholar working on Cognitive Neuroscience, Molecular Biology and Pharmacology. According to data from OpenAlex, Kun Feng has authored 49 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Cognitive Neuroscience, 12 papers in Molecular Biology and 11 papers in Pharmacology. Recurrent topics in Kun Feng's work include Functional Brain Connectivity Studies (14 papers), Fungal Biology and Applications (9 papers) and Optical Imaging and Spectroscopy Techniques (9 papers). Kun Feng is often cited by papers focused on Functional Brain Connectivity Studies (14 papers), Fungal Biology and Applications (9 papers) and Optical Imaging and Spectroscopy Techniques (9 papers). Kun Feng collaborates with scholars based in China, Macao and Canada. Kun Feng's co-authors include Shaoping Li, J. Zhao, Feng Yang, Pozi Liu, Xiaoqian Zhang, Chenyu Shen, Qingjing Ye, Hongjian Wan, Yuejian Yang and Guozhi Zhou and has published in prestigious journals such as PLoS ONE, NeuroImage and Journal of Agricultural and Food Chemistry.

In The Last Decade

Kun Feng

44 papers receiving 1.2k citations

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Kun Feng 451 357 336 207 173 49 1.2k
Francesco Napolitano 422 0.9× 1.0k 2.8× 271 0.8× 187 0.9× 42 0.2× 80 2.7k
Qun Lu 252 0.6× 1.4k 3.9× 219 0.7× 265 1.3× 14 0.1× 88 3.2k
Walter K.K. Ho 584 1.3× 642 1.8× 401 1.2× 89 0.4× 25 0.1× 89 2.3k
L. Antunes 99 0.2× 244 0.7× 131 0.4× 170 0.8× 60 0.3× 102 1.9k
Mijung Yeom 175 0.4× 603 1.7× 279 0.8× 87 0.4× 22 0.1× 70 1.8k
Flávia Rodrigues da Silva 96 0.2× 460 1.3× 519 1.5× 90 0.4× 17 0.1× 26 1.5k
Geun Hee Seol 259 0.6× 552 1.5× 127 0.4× 306 1.5× 14 0.1× 77 2.0k
Tetsuro Nagasawa 148 0.3× 362 1.0× 85 0.3× 430 2.1× 28 0.2× 50 1.9k
Pan Xu 93 0.2× 204 0.6× 124 0.4× 133 0.6× 23 0.1× 16 727
Shafiqur Rahman 210 0.5× 1.1k 3.2× 206 0.6× 156 0.8× 9 0.1× 102 2.2k

Countries citing papers authored by Kun Feng

Since Specialization
Citations

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

Fields of papers citing papers by Kun Feng

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kun Feng

This figure shows the co-authorship network connecting the top 25 collaborators of Kun Feng. A scholar is included among the top collaborators of Kun Feng 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 Kun Feng. Kun Feng 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.
Dong, Hui, et al.. (2024). O-GlcNAcylated RALY Contributes to Hepatocellular Carcinoma Cells Proliferation by Regulating USP22 mRNA Nuclear Export. International Journal of Biological Sciences. 20(9). 3675–3690. 3 indexed citations
2.
Luo, Jing, Ting Zou, Jing Li, et al.. (2023). Transcriptional patterns of the cortical Morphometric Inverse Divergence in first-episode, treatment-naïve early-onset schizophrenia. NeuroImage. 285. 120493–120493. 7 indexed citations
3.
Zou, Ting, Jing Luo, Shuang Hu, et al.. (2023). Cortical structural changes of morphometric similarity network in early-onset schizophrenia correlate with specific transcriptional expression patterns. BMC Medicine. 21(1). 479–479. 15 indexed citations
4.
Ma, Xiangyun, Pozi Liu, Samuel Law, et al.. (2022). Characteristics of psychomotor retardation distinguishes patients with depression using multichannel near-infrared spectroscopy and finger tapping task. Journal of Affective Disorders. 318. 255–262. 4 indexed citations
5.
Ren, Yufei, et al.. (2022). The promising fNIRS: Uncovering the function of prefrontal working memory networks based on multi-cognitive tasks. Frontiers in Psychiatry. 13. 985076–985076. 5 indexed citations
6.
7.
Chen, Guifang, Kun Feng, Xiaoqian Zhang, et al.. (2021). Brain activation during verbal fluency task in type II bipolar disorder patients: a near-infrared spectroscopy study. Psychiatry Research. 298. 113762–113762. 8 indexed citations
8.
9.
Feng, Kun, Samuel Law, Nisha Ravindran, et al.. (2020). Differentiating between bipolar and unipolar depression using prefrontal activation patterns: Promising results from functional near infrared spectroscopy (fNIRS) findings. Journal of Affective Disorders. 281. 476–484. 38 indexed citations
10.
Feng, Kun, Chenyu Shen, Xiangyun Ma, et al.. (2019). Effects of music therapy on major depressive disorder: A study of prefrontal hemodynamic functions using fNIRS. Psychiatry Research. 275. 86–93. 37 indexed citations
11.
Ma, Xiangyun, Kun Feng, Gaoxiang Sun, et al.. (2017). Near-Infrared Spectroscopy Reveals Abnormal Hemodynamics in the Left Dorsolateral Prefrontal Cortex of Menopausal Depression Patients. Disease Markers. 2017. 1–10. 18 indexed citations
12.
Shen, Chenyu, Xiaomin Liu, Xiaoqian Zhang, et al.. (2017). Improvement of Orbitofrontal Cortex Function Associated with Blephrospasm Symptom Remission. European Neurology. 77(5-6). 288–294.
13.
Chen, Zhiwen, Kun Feng, Corrinne E. Grover, et al.. (2016). Chloroplast DNA Structural Variation, Phylogeny, and Age of Divergence among Diploid Cotton Species. PLoS ONE. 11(6). e0157183–e0157183. 39 indexed citations
14.
Feng, Kun, Jiahong Yu, Yuan Cheng, et al.. (2016). The SOD Gene Family in Tomato: Identification, Phylogenetic Relationships, and Expression Patterns. Frontiers in Plant Science. 7. 1279–1279. 141 indexed citations
15.
Cheng, Yuan, Jiahong Yu, Zhuping Yao, et al.. (2016). Putative WRKYs associated with regulation of fruit ripening revealed by detailed expression analysis of the WRKY gene family in pepper. Scientific Reports. 6(1). 39000–39000. 53 indexed citations
16.
Yu, Jiahong, Yuan Cheng, Kun Feng, et al.. (2016). Genome-Wide Identification and Expression Profiling of Tomato Hsp20 Gene Family in Response to Biotic and Abiotic Stresses. Frontiers in Plant Science. 7. 1215–1215. 90 indexed citations
17.
Liu, Xiaomin, Gaoxiang Sun, Xiaoqian Zhang, et al.. (2014). Relationship between the prefrontal function and the severity of the emotional symptoms during a verbal fluency task in patients with major depressive disorder: A multi-channel NIRS study. Progress in Neuro-Psychopharmacology and Biological Psychiatry. 54. 114–121. 70 indexed citations
18.
Meng, Lan‐Zhen, Baoqin Lin, Bo Wang, et al.. (2013). Mycelia extracts of fungal strains isolated from Cordyceps sinensis differently enhance the function of RAW 264.7 macrophages. Journal of Ethnopharmacology. 148(3). 818–825. 30 indexed citations
19.
Qiu, Haitang, Yufeng Gao, Yixiao Fu, et al.. (2012). Changes in the expression of hippocampal proteins in rats with recrudescence of morphine addiction. Experimental and Therapeutic Medicine. 5(3). 825–829. 4 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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