Kunkai Su

2.8k total citations
29 papers, 366 citations indexed

About

Kunkai Su is a scholar working on Molecular Biology, Cancer Research and Epidemiology. According to data from OpenAlex, Kunkai Su has authored 29 papers receiving a total of 366 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 8 papers in Cancer Research and 7 papers in Epidemiology. Recurrent topics in Kunkai Su's work include Smoking Behavior and Cessation (5 papers), Nicotinic Acetylcholine Receptors Study (5 papers) and Cancer-related molecular mechanisms research (4 papers). Kunkai Su is often cited by papers focused on Smoking Behavior and Cessation (5 papers), Nicotinic Acetylcholine Receptors Study (5 papers) and Cancer-related molecular mechanisms research (4 papers). Kunkai Su collaborates with scholars based in China, United States and France. Kunkai Su's co-authors include Ming D. Li, Yunlong Ma, Wenji Yuan, Lanjuan Li, Wenyan Cui, Yijia Lou, Jun Zhu, Thomas J. Payne, Zhihong Zhu and Zhixiang Zhu and has published in prestigious journals such as PLoS ONE, Scientific Reports and Journal of Bacteriology.

In The Last Decade

Kunkai Su

29 papers receiving 361 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Kunkai Su China 12 135 87 64 45 43 29 366
Qadar Pasha India 9 80 0.6× 126 1.4× 49 0.8× 32 0.7× 78 1.8× 27 380
Wenqian Chen China 9 166 1.2× 72 0.8× 81 1.3× 38 0.8× 16 0.4× 18 330
Mónica Porras Spain 8 144 1.1× 43 0.5× 31 0.5× 10 0.2× 46 1.1× 13 344
Isabel Casimiro United States 12 119 0.9× 33 0.4× 80 1.3× 18 0.4× 142 3.3× 19 522
Tina Le Canada 8 132 1.0× 26 0.3× 39 0.6× 23 0.5× 53 1.2× 16 319
Matthias Banasch Germany 14 144 1.1× 31 0.4× 153 2.4× 24 0.5× 85 2.0× 22 542
Guy Chung‐Faye United Kingdom 10 87 0.6× 117 1.3× 110 1.7× 16 0.4× 34 0.8× 22 430

Countries citing papers authored by Kunkai Su

Since Specialization
Citations

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

Fields of papers citing papers by Kunkai Su

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kunkai Su

This figure shows the co-authorship network connecting the top 25 collaborators of Kunkai Su. A scholar is included among the top collaborators of Kunkai Su 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 Kunkai Su. Kunkai Su 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, Tao, Xueyao Wang, Kunkai Su, et al.. (2025). Comprehensive analysis of metabolism-related gene biomarkers reveals their impact on the diagnosis and prognosis of triple-negative breast cancer. BMC Cancer. 25(1). 668–668. 1 indexed citations
2.
Xu, Wei, Liying Zhang, Xiaoliang Qian, et al.. (2024). A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data. Scientific Reports. 14(1). 26705–26705. 1 indexed citations
3.
Zhu, Danhua, Kunkai Su, Xiaoxi Ouyang, et al.. (2024). Mechanisms and clinical landscape of N6-methyladenosine (m6A) RNA modification in gastrointestinal tract cancers. Molecular and Cellular Biochemistry. 479(7). 1553–1570. 13 indexed citations
4.
Zhu, Danhua, Xiaoxi Ouyang, Yanhong Zhang, et al.. (2023). A promising new cancer marker: Long noncoding RNA EGFR-AS1. Frontiers in Oncology. 13. 1130472–1130472. 6 indexed citations
5.
Su, Kunkai, Xuehua Zheng, Christian Bréchot, et al.. (2022). Five-lipoxygenase-activating protein-mediated CYLD attenuation is a candidate driver in hepatic malignant lesion. Frontiers in Oncology. 12. 912881–912881. 1 indexed citations
6.
Su, Kunkai, et al.. (2021). Association of Vitamin D Receptor Gene Polymorphisms with Metabolic Syndrome in Chinese Children. International Journal of General Medicine. Volume 14. 57–66. 13 indexed citations
7.
Zhang, Bin, Haijun Han, Kunkai Su, et al.. (2019). Identification and characterization of SEC24D as a susceptibility gene for hepatitis B virus infection. Scientific Reports. 9(1). 13425–13425. 3 indexed citations
8.
Yao, Yinghao, Yunlong Ma, Kunkai Su, et al.. (2018). Detection of Significant Association Between Variants in Cannabinoid Receptor 1 Gene (CNR1) and Personality in African–American Population. Frontiers in Genetics. 9. 199–199. 12 indexed citations
9.
Liu, Qiang, Haijun Han, Yinghao Yao, et al.. (2018). Association and cis-mQTL analysis of variants in CHRNA3-A5, CHRNA7, CHRNB2, and CHRNB4 in relation to nicotine dependence in a Chinese Han population. Translational Psychiatry. 8(1). 83–83. 17 indexed citations
10.
Ma, Yunlong, Wen Li, Wenyan Cui, et al.. (2017). Prevalence of Cigarette Smoking and Nicotine Dependence in Men and Women Residing in Two Provinces in China. Frontiers in Psychiatry. 8. 254–254. 32 indexed citations
11.
Li, Wen, Haijun Han, Qiang Liu, et al.. (2017). Significant association of the CHRNB3-CHRNA6 gene cluster with nicotine dependence in the Chinese Han population. Scientific Reports. 7(1). 9745–9745. 10 indexed citations
12.
Ma, Yunlong, et al.. (2016). Meta-Analysis of the COMT Val158Met Polymorphism in Major Depressive Disorder: Effect of Ethnicity. Journal of Neuroimmune Pharmacology. 11(3). 434–445. 38 indexed citations
13.
Li, Tong, Ke Jin, Danyan Zhu, et al.. (2016). Premalignant alteration assessment in liver-like tissue derived from embryonic stem cells by aristolochic acid I exposure. Oncotarget. 7(48). 78872–78882. 7 indexed citations
14.
Su, Kunkai, Qi Wang, Dasong Hua, et al.. (2016). MicroRNA-674-5p/5-LO axis involved in autoimmune reaction of Concanavalin A-induced acute mouse liver injury. Toxicology Letters. 258. 101–107. 26 indexed citations
15.
Su, Kunkai, et al.. (2016). Association of STAT4 polymorphisms with hepatitis B virus infection and clearance in Chinese Han population. Amino Acids. 48(11). 2589–2598. 9 indexed citations
16.
Jin, Ke, Kunkai Su, Tong Li, et al.. (2016). Hepatic Premalignant Alterations Triggered by Human Nephrotoxin Aristolochic Acid I in Canines. Cancer Prevention Research. 9(4). 324–334. 27 indexed citations
17.
Ma, Yunlong, et al.. (2015). The significant association of Taq1A genotypes in DRD2/ANKK1 with smoking cessation in a large-scale meta-analysis of Caucasian populations. Translational Psychiatry. 5(12). e686–e686. 15 indexed citations
18.
Zhu, Zhixiang, Zhihong Zhu, Wenyan Cui, et al.. (2013). Development of GMDR-GPU for Gene-Gene Interaction Analysis and Its Application to WTCCC GWAS Data for Type 2 Diabetes. PLoS ONE. 8(4). e61943–e61943. 50 indexed citations
19.
Hui, Hongxiang, Xiaoguang Xu, Wanying Yang, et al.. (2012). Alterations in fecal lactobacillus and bifidobacterium species are associated with differences in cholesterol metabolism in type 2 diabetic patients. 15th International & 14th European Congress of Endocrinology. 29. 1 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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