Moo-Young Eun

1.6k total citations
32 papers, 743 citations indexed

About

Moo-Young Eun is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Moo-Young Eun has authored 32 papers receiving a total of 743 indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Plant Science, 19 papers in Genetics and 8 papers in Molecular Biology. Recurrent topics in Moo-Young Eun's work include Genetic Mapping and Diversity in Plants and Animals (19 papers), GABA and Rice Research (14 papers) and Rice Cultivation and Yield Improvement (11 papers). Moo-Young Eun is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (19 papers), GABA and Rice Research (14 papers) and Rice Cultivation and Yield Improvement (11 papers). Moo-Young Eun collaborates with scholars based in South Korea, United States and China. Moo-Young Eun's co-authors include Susan R. McCouch, Dong‐Suk Park, Hee‐Wan Kang, Seung-Joo Go, Jae-Keun Sohn, Kyung‐Min Kim, Hyeonso Ji, Hee‐Jong Koh, Young‐Sam Kwon and Jang-Ho Hahn and has published in prestigious journals such as Genetics, Theoretical and Applied Genetics and Plant and Cell Physiology.

In The Last Decade

Moo-Young Eun

31 papers receiving 695 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Moo-Young Eun South Korea 15 671 326 220 42 38 32 743
Stig Tuvesson Sweden 10 735 1.1× 295 0.9× 189 0.9× 41 1.0× 27 0.7× 10 791
Marie-Françoise Gautier France 9 871 1.3× 253 0.8× 300 1.4× 35 0.8× 53 1.4× 10 1.0k
Katherine S. Caldwell United States 9 968 1.4× 350 1.1× 210 1.0× 46 1.1× 12 0.3× 9 1.1k
T. G. Krishna India 13 668 1.0× 195 0.6× 242 1.1× 22 0.5× 32 0.8× 23 786
H. Witsenboer Netherlands 12 488 0.7× 181 0.6× 223 1.0× 62 1.5× 54 1.4× 14 673
Marguerite Rodier-Goud France 15 488 0.7× 205 0.6× 302 1.4× 59 1.4× 9 0.2× 28 714
Aiping Zheng China 17 518 0.8× 109 0.3× 303 1.4× 72 1.7× 20 0.5× 36 698
S. R. Wessler United States 4 502 0.7× 147 0.5× 372 1.7× 20 0.5× 12 0.3× 6 645
Houxiang Kang China 19 1.1k 1.6× 482 1.5× 420 1.9× 108 2.6× 26 0.7× 40 1.3k
Yumei Dong China 15 573 0.9× 163 0.5× 144 0.7× 33 0.8× 10 0.3× 37 695

Countries citing papers authored by Moo-Young Eun

Since Specialization
Citations

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

Fields of papers citing papers by Moo-Young Eun

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Moo-Young Eun. 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 Moo-Young Eun. The network helps show where Moo-Young Eun may publish in the future.

Co-authorship network of co-authors of Moo-Young Eun

This figure shows the co-authorship network connecting the top 25 collaborators of Moo-Young Eun. A scholar is included among the top collaborators of Moo-Young Eun 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 Moo-Young Eun. Moo-Young Eun 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.
Park, Sung Han, Hyemin Lim, Ung-Han Yoon, et al.. (2014). Wound-inducible expression of the OsDof1 gene promoter in a Ds insertion mutant and transgenic plants. Plant Biotechnology Reports. 8(4). 305–313. 10 indexed citations
2.
Oh, Sung Aeong, et al.. (2013). Evaluation of Gene Flow from GM to Non-GM Rice. Plant Breeding and Biotechnology. 1(2). 162–170. 4 indexed citations
3.
Zhao, Xinhua, Qin Yang, Suk‐Man Kim, et al.. (2013). Comparison and Analysis of QTLs, Epistatic Effects and QTL×Environment Interactions for Yield Traits Using DH and RILs Populations in Rice. Journal of Integrative Agriculture. 12(2). 198–208. 6 indexed citations
4.
Ji, Hyeonso, et al.. (2012). Development of rice molecular genetic and physical map using PCR-based DNA markers with the recombinant inbred population derived from Milyang23/Gihobyeo cross.. Korean Journal of Breeding Science. 44(3). 273–281. 4 indexed citations
5.
Zhao, Xinhua, Qin Yang, Suk‐Man Kim, et al.. (2010). Comparison and analysis of main effects, epistatic effects, and QTL × environment interactions of QTLs for agronomic traits using DH and RILs populations in rice. Journal of Crop Science and Biotechnology. 13(4). 235–241. 2 indexed citations
6.
Yang, Qin, Suk‐Man Kim, Xinhua Zhao, et al.. (2010). Identification for quantitative trait loci controlling grain shattering in rice. Genes & Genomics. 32(2). 173–180. 17 indexed citations
7.
Kang, Hong‐Gyu, et al.. (2010). Transgenic rice plants carrying RNA interference constructs of AOS (allene oxide synthase) genes show severe male sterility. Plant Breeding. 129(6). 647–651. 20 indexed citations
8.
9.
Eun, Moo-Young, et al.. (2007). Mapping QTLs related to salinity tolerance of rice at the young seedling stage. Plant Breeding. 126(1). 43–46. 74 indexed citations
10.
Yi, Gihwan, Dong-Soo Park, Eunsook Chung, et al.. (2006). Pedigree analysis of 17 high quality Korean rice cultivars using web database systems.. The Korean Journal of Crop Science. 51(6). 554–564. 1 indexed citations
11.
Yi, Gihwan, Junho Choi, Kshirod K. Jena, et al.. (2006). Morphological and molecular characterization of a new frizzy panicle mutant, “fzp-9(t)”, in rice (Oryza sativa L.). Hereditas. 142(2005). 92–97. 20 indexed citations
12.
Hong, Sungwon, et al.. (2006). CACTA and MITE Transposon Distributions on a Genetic Map of Rice Using F15 RILs Derived from Milyang 23 and Gihobyeo Hybrids. Molecules and Cells. 21(3). 360–366. 19 indexed citations
13.
Eun, Moo-Young, et al.. (2004). QTL for Quality Properties in the Milyang23 $\times$ Gyhobyeo Recombinant Inbred Lines by Different Locations. The Korean Journal of Crop Science. 49(6). 539–545. 1 indexed citations
14.
Kang, Hee‐Wan, Dong‐Suk Park, Seung-Joo Go, & Moo-Young Eun. (2002). Fingerprinting of Diverse Genomes Using PCR with Universal Rice Primers Generated from Repetitive Sequence of Korean Weedy Rice. Molecules and Cells. 13(2). 281–287. 111 indexed citations
15.
16.
Lim, Jaechul, Chan Soo Shin, Eun Joo Chung, et al.. (2000). Analysis of expressed sequence tags from Brassica rapa L. ssp. pekinensis.. PubMed. 10(4). 399–404. 5 indexed citations
17.
Kim, Ju‐Kon, Ray Wü, Rebecca S. Boston, et al.. (1999). Molecular and genetic analysis of transgenic rice plants expressing the maize ribosome-inactivating protein b-32 gene and the herbicide resistance bar gene. Molecular Breeding. 5(2). 85–94. 33 indexed citations
18.
Kang, Hyeon‐Jung, Yong‐Gu Cho, Young‐Tae Lee, et al.. (1998). QTL Mapping of Genes Related with Grain Chemical Properties Based on Molecular Map of Rice. The Korean Journal of Crop Science. 43(4). 199–204. 11 indexed citations
19.
Yang, Kwang‐Yeol, et al.. (1997). Structure and Expression of the AWI 31 Gene Specifically Induced by Wounding in Arabidopsis thaliana. Molecules and Cells. 7(1). 131–135. 14 indexed citations
20.
Eun, Moo-Young, et al.. (1994). The semidwarf gene, sd-1, of rice (Oryza sativa L.). II. Molecular mapping and marker-assisted selection. Theoretical and Applied Genetics. 89(1). 54–59. 98 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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