Tae-Jeong Choi

811 total citations
73 papers, 583 citations indexed

About

Tae-Jeong Choi is a scholar working on Genetics, Animal Science and Zoology and Small Animals. According to data from OpenAlex, Tae-Jeong Choi has authored 73 papers receiving a total of 583 indexed citations (citations by other indexed papers that have themselves been cited), including 52 papers in Genetics, 28 papers in Animal Science and Zoology and 15 papers in Small Animals. Recurrent topics in Tae-Jeong Choi's work include Genetic and phenotypic traits in livestock (50 papers), Genetic Mapping and Diversity in Plants and Animals (22 papers) and Animal Behavior and Welfare Studies (15 papers). Tae-Jeong Choi is often cited by papers focused on Genetic and phenotypic traits in livestock (50 papers), Genetic Mapping and Diversity in Plants and Animals (22 papers) and Animal Behavior and Welfare Studies (15 papers). Tae-Jeong Choi collaborates with scholars based in South Korea, United States and Australia. Tae-Jeong Choi's co-authors include Byoungho Park, Seungchang Kim, Si-Dong Kim, Seung Hwan Lee, Chang–Gwon Dang, Kwang‐Hyun Cho, Aditi Sharma, Chang-Hee Do, Seung Soo Lee and Soo‐Bong Park and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Sensors.

In The Last Decade

Tae-Jeong Choi

62 papers receiving 548 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tae-Jeong Choi South Korea 12 466 245 139 99 59 73 583
Radovan Kasarda Slovakia 10 514 1.1× 181 0.7× 130 0.9× 55 0.6× 24 0.4× 123 613
Chang–Gwon Dang South Korea 11 379 0.8× 141 0.6× 84 0.6× 154 1.6× 26 0.4× 40 495
Rasoul Vaez Torshizi Iran 14 439 0.9× 232 0.9× 151 1.1× 52 0.5× 28 0.5× 79 603
Hassan Mehrabani-Yeganeh Iran 13 407 0.9× 192 0.8× 144 1.0× 52 0.5× 21 0.4× 23 596
Lúcio Flávio Macêdo Mota Brazil 16 512 1.1× 307 1.3× 258 1.9× 63 0.6× 26 0.4× 74 727
Takeshi Honda Japan 12 375 0.8× 118 0.5× 104 0.7× 66 0.7× 23 0.4× 38 479
Phil Bowman Australia 10 652 1.4× 296 1.2× 166 1.2× 101 1.0× 83 1.4× 10 832
A.B. Samoré Italy 17 561 1.2× 238 1.0× 347 2.5× 44 0.4× 87 1.5× 44 672
Isabelle Palhière France 17 559 1.2× 106 0.4× 223 1.6× 85 0.9× 17 0.3× 36 686
Takuro Oikawa Japan 13 452 1.0× 253 1.0× 138 1.0× 28 0.3× 78 1.3× 56 550

Countries citing papers authored by Tae-Jeong Choi

Since Specialization
Citations

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

Fields of papers citing papers by Tae-Jeong Choi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tae-Jeong Choi. 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 Tae-Jeong Choi. The network helps show where Tae-Jeong Choi may publish in the future.

Co-authorship network of co-authors of Tae-Jeong Choi

This figure shows the co-authorship network connecting the top 25 collaborators of Tae-Jeong Choi. A scholar is included among the top collaborators of Tae-Jeong Choi 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 Tae-Jeong Choi. Tae-Jeong Choi 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.
Koo, Yang Mo, et al.. (2023). Genomic evaluation of carcass traits of Korean beef cattle Hanwoo using a single-step marker effect model. Journal of Animal Science. 101. 2 indexed citations
2.
3.
Dang, Chang–Gwon, et al.. (2022). Case Study: Improving the Quality of Dairy Cow Reconstruction with a Deep Learning-Based Framework. Sensors. 22(23). 9325–9325. 4 indexed citations
4.
Lee, Soo-Hyun, Chang–Gwon Dang, Mi Na Park, et al.. (2022). Comparison of the estimated breeding value and accuracy by imputation reference Beadchip platform and scaling factor of the genomic relationship matrix in Hanwoo cattle. Korean Journal of Agricultural Science. 49(3). 431–440.
5.
Lim, Kyu‐Sang, Hyungchul Kim, Bong‐Hwan Choi, et al.. (2021). Identification of Monoallelically Expressed Genes Associated with Economic Traits in Hanwoo (Korean Native Cattle). Animals. 12(1). 84–84. 2 indexed citations
6.
Seo, Dongwon, et al.. (2021). Accuracy of genotype imputation based on reference population size and marker density in Hanwoo cattle. Journal of Animal Science and Technology. 63(6). 1232–1246. 2 indexed citations
7.
Park, Mi Na, Dongwon Seo, Ki Yong Chung, et al.. (2020). Genomic partitioning of growth traits using a high-density single nucleotide polymorphism array in Hanwoo (Korean cattle). Asian-Australasian Journal of Animal Sciences. 33(10). 1558–1565. 2 indexed citations
8.
Kim, So Yeon, et al.. (2019). The effect of progeny numbers and pedigree depth on the accuracy of the EBV with the BLUP method. Korean Journal of Agricultural Science. 46(2). 293–301. 2 indexed citations
9.
Hong, Joon Ki, et al.. (2019). Genetic association between sow longevity and social genetic effects on growth in pigs. Asian-Australasian Journal of Animal Sciences. 32(8). 1077–1083. 5 indexed citations
10.
Kim, Kwondo, Jaehoon Jung, Kelsey Caetano-Anollés, et al.. (2018). Artificial selection increased body weight but induced increase of runs of homozygosity in Hanwoo cattle. PLoS ONE. 13(3). e0193701–e0193701. 6 indexed citations
11.
Lee, Mi Jin, et al.. (2018). Comparison of meat quality characteristics of Yorkshire, Duroc, Pietrain and Crossbred Pigs (Duroc × Pietrain). Journal of the Korea Academia-Industrial cooperation Society. 19(11). 116–125. 2 indexed citations
12.
Hong, Joon Ki, Yong Dae Jeong, Eun Seok Cho, et al.. (2017). A genome-wide association study of social genetic effects in Landrace pigs. Asian-Australasian Journal of Animal Sciences. 31(6). 784–790. 8 indexed citations
13.
Lee, Sung-Soo, et al.. (2016). Growth Curve Parameters for Body Weight by Sex in Korean Native Goat. 27(3). 152–158. 3 indexed citations
14.
Seo, Dongwon, et al.. (2016). Genome-wide linkage disequilibrium and past effective population size in three Korean cattle breeds. Animal Genetics. 48(1). 85–89. 8 indexed citations
15.
Choi, Tae-Jeong, et al.. (2015). Models for Estimating Genetic Parameters of Milk Production Traits Using Random Regression Models in Korean Holstein Cattle. Asian-Australasian Journal of Animal Sciences. 29(5). 607–614. 11 indexed citations
16.
Kim, Sang Bum, et al.. (2015). Differential expression of circulating microRNAs in lactating Holstein and Jersey cows exposed to heat stress. SHILAP Revista de lepidopterología. 39(4). 221–232. 2 indexed citations
17.
Choi, Tae-Jeong, et al.. (2012). Determination of Genetic Diversity among Korean Hanwoo Cattle Based on Physical Characteristics. Asian-Australasian Journal of Animal Sciences. 25(9). 1205–1215. 4 indexed citations
18.
Choi, Tae-Jeong, et al.. (2006). Estimation of growth curves of body weight and measurements for Hanwoo steers.. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006. 13–16. 1 indexed citations
19.
Seo, Kang-Seok, et al.. (2006). Estimation of genetic parameters for change of test day records on the milk production and SCS using Random Regression model of the Holstein cattle in Korea.. Proceedings of the 8th World Congress on Genetics Applied to Livestock Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006. 1–51. 2 indexed citations
20.
Seo, Kang-Seok, et al.. (2006). Inbreeding and effective population size of Holstein in Korea.. 1–73.

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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