Kunhui He

482 total citations
22 papers, 297 citations indexed

About

Kunhui He is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Kunhui He has authored 22 papers receiving a total of 297 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Plant Science, 17 papers in Genetics and 2 papers in Molecular Biology. Recurrent topics in Kunhui He's work include Genetic Mapping and Diversity in Plants and Animals (17 papers), Genetics and Plant Breeding (10 papers) and Plant nutrient uptake and metabolism (7 papers). Kunhui He is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (17 papers), Genetics and Plant Breeding (10 papers) and Plant nutrient uptake and metabolism (7 papers). Kunhui He collaborates with scholars based in China, Mexico and Ethiopia. Kunhui He's co-authors include Liguo Chang, Jianchao Liu, Fanjun Chen, Lixing Yuan, Qingchun Pan, Pengcheng Li, Shutu Xu, Guohua Mi, Jiquan Xue and Wei Ren and has published in prestigious journals such as Scientific Reports, Journal of Experimental Botany and Advanced Science.

In The Last Decade

Kunhui He

22 papers receiving 288 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Kunhui He China	11	218	101	66	50	9	22	297
Jienan Han China	9	201 0.9×	56 0.6×	46 0.7×	65 1.3×	9 1.0×	26	234
Nina Opitz Germany	8	328 1.5×	81 0.8×	32 0.5×	122 2.4×	6 0.7×	8	361
Zhiyuan Yang China	10	219 1.0×	37 0.4×	100 1.5×	80 1.6×	4 0.4×	26	302
Yuxin Yang China	8	187 0.9×	55 0.5×	30 0.5×	89 1.8×	5 0.6×	26	237
Yujin Wu China	11	278 1.3×	183 1.8×	58 0.9×	113 2.3×	28 3.1×	23	367
T. Abadie Uruguay	8	282 1.3×	149 1.5×	48 0.7×	48 1.0×	6 0.7×	16	312
Yafan Zhao China	8	272 1.2×	51 0.5×	18 0.3×	97 1.9×	5 0.6×	15	297
Shyam Solanki United States	10	210 1.0×	36 0.4×	36 0.5×	57 1.1×	7 0.8×	32	262
Ryo Nishijima Japan	12	256 1.2×	107 1.1×	22 0.3×	58 1.2×	3 0.3×	26	283
Maria Genaleen Q. Diaz Philippines	10	319 1.5×	71 0.7×	12 0.2×	63 1.3×	8 0.9×	32	357

Countries citing papers authored by Kunhui He

Since Specialization

Citations

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

Fields of papers citing papers by Kunhui He

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kunhui He

This figure shows the co-authorship network connecting the top 25 collaborators of Kunhui He. A scholar is included among the top collaborators of Kunhui He 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 Kunhui He. Kunhui He is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wang, Jingxin, Liwei Liu, Kunhui He, et al.. (2025). Accurate genomic prediction for grain yield and grain moisture content of maize hybrids using multi‐environment data. Journal of Integrative Plant Biology. 67(5). 1379–1394. 1 indexed citations

Chen, Shoukun, Hao Zhang, Shuqiang Gao, et al.. (2025). Unveiling Salt Tolerance Mechanisms in Plants: Integrating the KANMB Machine Learning Model With Metabolomic and Transcriptomic Analysis. Advanced Science. 12(23). e2417560–e2417560. 6 indexed citations

He, Kunhui, Tingxi Yu, Shang Gao, et al.. (2025). Leveraging Automated Machine Learning for Environmental Data‐Driven Genetic Analysis and Genomic Prediction in Maize Hybrids. Advanced Science. 12(17). e2412423–e2412423. 7 indexed citations

He, Binsheng, et al.. (2025). A fusion model to predict the survival of colorectal cancer based on histopathological image and gene mutation. Scientific Reports. 15(1). 9677–9677. 11 indexed citations

Chang, Liguo, Kunhui He, & Jianchao Liu. (2024). Decoding the genetic basis of ear‐related traits in maize (Zea mays L.) using linkage mapping, association mapping and genomic prediction. Plant Breeding. 143(6). 840–856. 1 indexed citations

Chen, Shoukun, Kunhui He, Tingxi Yu, et al.. (2024). Exploring salt tolerance mechanisms using machine learning for transcriptomic insights: case study in Spartina alterniflora. Horticulture Research. 11(5). uhae082–uhae082. 8 indexed citations

Li, Pengcheng, Zhihai Zhang, Gui Xiao, et al.. (2024). Genomic basis determining root system architecture in maize. Theoretical and Applied Genetics. 137(5). 102–102. 6 indexed citations

Chen, Shoukun, Tingting Du, Kunhui He, et al.. (2024). The Spartina alterniflora genome sequence provides insights into the salt‐tolerance mechanisms of exo‐recretohalophytes. Plant Biotechnology Journal. 22(9). 2558–2574. 12 indexed citations

He, Kunhui, Yakun Zhang, Wei Ren, et al.. (2023). QTL mapping and transcriptome analysis identify candidate genes influencing water–nitrogen interaction in maize. The Crop Journal. 11(6). 1872–1883. 5 indexed citations

10.

He, Kunhui, Zheng Zhao, Wei Ren, et al.. (2023). Mining genes regulating root system architecture in maize based on data integration analysis. Theoretical and Applied Genetics. 136(6). 127–127. 8 indexed citations

11.

Bing, Pingping, Meijun Zhang, Geng Tian, et al.. (2023). MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm. Computational and Structural Biotechnology Journal. 21. 1414–1423. 31 indexed citations

12.

Liu, Xiaoyang, Kunhui He, Farhan Ali, et al.. (2023). Genetic dissection of N use efficiency using maize inbred lines and testcrosses. The Crop Journal. 11(4). 1242–1250. 5 indexed citations

13.

Ren, Wei, Longfei Zhao, Jiaxing Liang, et al.. (2022). Genome-wide dissection of changes in maize root system architecture during modern breeding. Nature Plants. 8(12). 1408–1422. 76 indexed citations

14.

Chen, Zhe, Junli Sun, Dongdong Li, et al.. (2021). Plasticity of root anatomy during domestication of a maize-teosinte derived population. Journal of Experimental Botany. 73(1). 139–153. 18 indexed citations

15.

Zhao, Zhixin, Kunhui He, Yanan Li, et al.. (2019). Evaluation of Yield-Based Low Nitrogen Tolerance Indices for Screening Maize (Zea mays L.) Inbred Lines. Agronomy. 9(5). 240–240. 13 indexed citations

16.

Li, Ting, Jianzhou Qu, Yahui Wang, et al.. (2018). Genetic characterization of inbred lines from Shaan A and B groups for identifying loci associated with maize grain yield. BMC Genetics. 19(1). 63–63. 22 indexed citations

17.

He, Kunhui, et al.. (2018). QTL mapping and genetic analysis for maize kernel size and weight in multi-environments. Euphytica. 214(7). 14 indexed citations

18.

He, Kunhui, Liguo Chang, Dong Yuan, et al.. (2017). Identification of quantitative trait loci for agronomic and physiological traits in maize (Zea mays L.) under high-nitrogen and low-nitrogen conditions. Euphytica. 214(1). 15 indexed citations

19.

Cui, Tingting, et al.. (2017). QTL mapping for leaf area in maize (Zea mays L.) under multi-environments. Journal of Integrative Agriculture. 16(4). 800–808. 17 indexed citations

20.

Chang, Liguo, et al.. (2016). Mapping of QTLs for Leaf Angle in Maize under Different Environments. 24(4). 55. 6 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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