Gul Won Jang

923 total citations
38 papers, 624 citations indexed

About

Gul Won Jang is a scholar working on Genetics, Animal Science and Zoology and Cancer Research. According to data from OpenAlex, Gul Won Jang has authored 38 papers receiving a total of 624 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Genetics, 13 papers in Animal Science and Zoology and 11 papers in Cancer Research. Recurrent topics in Gul Won Jang's work include Genetic and phenotypic traits in livestock (24 papers), Genetic Mapping and Diversity in Plants and Animals (17 papers) and Cancer-related molecular mechanisms research (10 papers). Gul Won Jang is often cited by papers focused on Genetic and phenotypic traits in livestock (24 papers), Genetic Mapping and Diversity in Plants and Animals (17 papers) and Cancer-related molecular mechanisms research (10 papers). Gul Won Jang collaborates with scholars based in South Korea, Australia and United States. Gul Won Jang's co-authors include Bong‐Hwan Choi, Seung Hwan Lee, Jong‐Eun Park, Dajeong Lim, Duhak Yoon, Krishnamoorthy Srikanth, Ho Young Chung, Kyung‐Tai Lee, Cedric Gondro and Kwang Soo Kim and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Gul Won Jang

34 papers receiving 608 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Gul Won Jang South Korea 13 426 195 176 170 61 38 624
Mohammad Moradi-Shahrbabak Iran 13 432 1.0× 149 0.8× 142 0.8× 111 0.7× 32 0.5× 17 612
Kacper Żukowski Poland 14 408 1.0× 164 0.8× 240 1.4× 157 0.9× 56 0.9× 54 660
Lucas Lima Verardo Brazil 13 398 0.9× 131 0.7× 109 0.6× 151 0.9× 46 0.8× 41 539
Longchao Zhang China 16 466 1.1× 233 1.2× 223 1.3× 147 0.9× 51 0.8× 51 671
Gabriel Costa Monteiro Moreira Brazil 14 392 0.9× 125 0.6× 194 1.1× 197 1.2× 40 0.7× 43 569
Natalia Sevane Spain 15 408 1.0× 91 0.5× 146 0.8× 178 1.0× 20 0.3× 39 635
Arianna Manunza Spain 15 375 0.9× 91 0.5× 78 0.4× 73 0.4× 36 0.6× 38 480
Anne-Sophie Van Laere Belgium 8 769 1.8× 185 0.9× 379 2.2× 228 1.3× 61 1.0× 12 1.1k
Larissa Fernanda Simielli Fonseca Brazil 16 366 0.9× 131 0.7× 148 0.8× 147 0.9× 34 0.6× 43 518
E. Alves Spain 15 456 1.1× 82 0.4× 176 1.0× 245 1.4× 62 1.0× 23 643

Countries citing papers authored by Gul Won Jang

Since Specialization
Citations

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

Fields of papers citing papers by Gul Won Jang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gul Won Jang. 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 Gul Won Jang. The network helps show where Gul Won Jang may publish in the future.

Co-authorship network of co-authors of Gul Won Jang

This figure shows the co-authorship network connecting the top 25 collaborators of Gul Won Jang. A scholar is included among the top collaborators of Gul Won Jang 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 Gul Won Jang. Gul Won Jang 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.
2.
Kim, Chan Ho, et al.. (2022). Effect of alternative farrowing pens with temporary crating on the performance of lactating sows and their litters. Journal of Animal Science and Technology. 64(3). 574–587. 4 indexed citations
3.
Choo, Hyo-Jun, et al.. (2021). Genome-Wide Association Study Identifies 12 Loci Associated with Body Weight at Age 8 Weeks in Korean Native Chickens. Genes. 12(8). 1170–1170. 27 indexed citations
4.
5.
Srikanth, Krishnamoorthy, Jong‐Eun Park, Sang Yun Ji, et al.. (2020). Genome-Wide Transcriptome and Metabolome Analyses Provide Novel Insights and Suggest a Sex-Specific Response to Heat Stress in Pigs. Genes. 11(5). 540–540. 18 indexed citations
6.
Kumar, Himansu, Krishnamoorthy Srikanth, Hyo-Jun Choo, et al.. (2019). Transcriptome of Chicken Liver Tissues Reveals the Candidate Genes and Pathways Responsible for Adaptation into Two Different Climatic Conditions. Animals. 9(12). 1076–1076. 10 indexed citations
7.
Srikanth, Krishnamoorthy, Jae Min Kim, Kyung‐Tai Lee, et al.. (2019). Comprehensive genome and transcriptome analyses reveal genetic relationship, selection signature, and transcriptome landscape of small-sized Korean native Jeju horse. Scientific Reports. 9(1). 16672–16672. 19 indexed citations
8.
Srikanth, Krishnamoorthy, Dajeong Lim, Kyung‐Tai Lee, et al.. (2019). Serial gene co-expression network approach to mine biological meanings from integrated transcriptomes of the porcine endometrium during estrous cycle. Functional & Integrative Genomics. 20(1). 117–131. 1 indexed citations
9.
Srikanth, Krishnamoorthy, et al.. (2017). Transcriptome analysis and identification of significantly differentially expressed genes in Holstein calves subjected to severe thermal stress. International Journal of Biometeorology. 61(11). 1993–2008. 20 indexed citations
10.
Lim, Dajeong, Bong‐Hwan Choi, Yong Min Cho, et al.. (2016). Analysis of extended haplotype in Korean cattle (Hanwoo) population. BMB Reports. 49(9). 514–519. 8 indexed citations
11.
Lee, Kyung‐Tai, et al.. (2016). Genomic structure, expression and association study of the porcine FSD2. Molecular Biology Reports. 43(9). 1011–1018. 3 indexed citations
12.
Strucken, Eva M., Seung Hwan Lee, Gul Won Jang, Laércio R. Porto-Neto, & Cedric Gondro. (2015). Towards breed formation by island model divergence in Korean cattle. BMC Evolutionary Biology. 15(1). 284–284. 16 indexed citations
13.
Park, Chang-Min, et al.. (2014). Paternity Identification Using the Multiplex PCR with Microsatellite Markers in Chicken. Journal of Agriculture & Life Science. 48(2). 69–75. 1 indexed citations
14.
Lee, Seung Hwan, Bong‐Hwan Choi, Dajeong Lim, et al.. (2013). Genome-Wide Association Study Identifies Major Loci for Carcass Weight on BTA14 in Hanwoo (Korean Cattle). PLoS ONE. 8(10). e74677–e74677. 80 indexed citations
15.
Lee, Hyun‐Jeong, Mi Jang, Woori Kwak, et al.. (2013). Comparative Transcriptome Analysis of Adipose Tissues Reveals that ECM-Receptor Interaction Is Involved in the Depot-Specific Adipogenesis in Cattle. PLoS ONE. 8(6). e66267–e66267. 91 indexed citations
16.
Kim, Seungchang, Seung Hwan Lee, Gul Won Jang, et al.. (2013). Genetic Polymorphisms of the Bovine NOV Gene Are Significantly Associated with Carcass Traits in Korean Cattle. Asian-Australasian Journal of Animal Sciences. 26(6). 780–787. 1 indexed citations
17.
Choi, Bong‐Hwan, et al.. (2012). Detection of Quantitative Trait Loci Affecting Fat Deposition Traits in Pigs. Asian-Australasian Journal of Animal Sciences. 25(11). 1507–1510. 2 indexed citations
18.
Chung, Ho Young, et al.. (2007). Identification of microsatellite markers between SW71 and SW1881 on porcine chromosome 6. Animal Genetics. 38(1). 81–81. 1 indexed citations
19.
Chung, Ho Young, et al.. (2006). Isolation and Characterization of the Bovine Microsatellite Loci. Biochemical Genetics. 44(11-12). 518–532. 2 indexed citations
20.
Kim, Kwang Soo, et al.. (2005). Genetic structure of pig breeds from Korea and China using microsatellite loci analysis1. Journal of Animal Science. 83(10). 2255–2263. 89 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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