Sojeong Ka

2.2k total citations · 1 hit paper
19 papers, 1.5k citations indexed

About

Sojeong Ka is a scholar working on Molecular Biology, Genetics and Animal Science and Zoology. According to data from OpenAlex, Sojeong Ka has authored 19 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 7 papers in Genetics and 5 papers in Animal Science and Zoology. Recurrent topics in Sojeong Ka's work include Genetic Mapping and Diversity in Plants and Animals (6 papers), Genetic and phenotypic traits in livestock (4 papers) and Adipose Tissue and Metabolism (4 papers). Sojeong Ka is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (6 papers), Genetic and phenotypic traits in livestock (4 papers) and Adipose Tissue and Metabolism (4 papers). Sojeong Ka collaborates with scholars based in South Korea, United States and Sweden. Sojeong Ka's co-authors include Jae Bum Kim, Finn Hallböök, Leif Andersson, P.B. Siegel, Ted Sharpe, François Besnier, Michèle Tixier‐Boichard, Bertrand Bed’Hom, Per Jensen and Matthew T. Webster and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Diabetes.

In The Last Decade

Sojeong Ka

19 papers receiving 1.4k citations

Hit Papers

Whole-genome resequencing... 2010 2026 2015 2020 2010 250 500 750

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Sojeong Ka South Korea 13 638 600 292 227 205 19 1.5k
Dan Nonneman United States 27 952 1.5× 726 1.2× 364 1.2× 135 0.6× 276 1.3× 97 2.0k
Xiaohui Xie China 5 1.2k 1.8× 900 1.5× 82 0.3× 403 1.8× 216 1.1× 6 2.1k
Zhengrong Yuan China 19 404 0.6× 484 0.8× 93 0.3× 112 0.5× 139 0.7× 100 1.3k
Huabin Zhu China 26 365 0.6× 814 1.4× 207 0.7× 131 0.6× 227 1.1× 97 2.0k
Karen Plaut United States 21 421 0.7× 459 0.8× 137 0.5× 221 1.0× 181 0.9× 65 1.5k
Valérie Amarger France 17 845 1.3× 912 1.5× 417 1.4× 325 1.4× 178 0.9× 41 2.1k
Meiyu Xu China 20 271 0.4× 374 0.6× 82 0.3× 139 0.6× 121 0.6× 77 1.4k
Gonzalo Rincón United States 24 1.2k 1.9× 583 1.0× 279 1.0× 94 0.4× 470 2.3× 70 2.0k
Stephanie McKay United States 25 1.7k 2.7× 584 1.0× 254 0.9× 82 0.4× 430 2.1× 55 2.4k
Catherine W. Ernst United States 28 1.2k 1.8× 615 1.0× 611 2.1× 99 0.4× 356 1.7× 107 2.1k

Countries citing papers authored by Sojeong Ka

Since Specialization
Citations

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

Fields of papers citing papers by Sojeong Ka

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sojeong Ka

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

All Works

19 of 19 papers shown
1.
Hwang, Injae, Kyuri Jo, Kyung Cheul Shin, et al.. (2019). GABA-stimulated adipose-derived stem cells suppress subcutaneous adipose inflammation in obesity. Proceedings of the National Academy of Sciences. 116(24). 11936–11945. 63 indexed citations
2.
Kim, Soo‐Jin, Sojeong Ka, Jung-Woo Ha, et al.. (2017). Cattle genome-wide analysis reveals genetic signatures in trypanotolerant N’Dama. BMC Genomics. 18(1). 371–371. 31 indexed citations
3.
Ka, Sojeong, Sunho Lee, Yangrae Cho, et al.. (2017). HLAscan: genotyping of the HLA region using next-generation sequencing data. BMC Bioinformatics. 18(1). 258–258. 75 indexed citations
4.
Alfadda, Assim A., Reem M. Sallam, Rukhsana Gul, Injae Hwang, & Sojeong Ka. (2017). Endophilin A2: A Potential Link to Adiposity and Beyond.. PubMed. 40(11). 855–863. 2 indexed citations
5.
Seo, Minseok, Kelsey Caetano-Anollés, Sandra L. Rodriguez‐Zas, et al.. (2016). Comprehensive identification of sexually dimorphic genes in diverse cattle tissues using RNA-seq. BMC Genomics. 17(1). 81–81. 23 indexed citations
6.
Ka, Sojeong, Hyeonju Ahn, Minseok Seo, et al.. (2016). Status of dosage compensation of X chromosome in bovine genome. Genetica. 144(4). 435–444. 4 indexed citations
7.
Hwang, Injae, Yoon Jeong Park, Yeon‐Ran Kim, et al.. (2015). Alteration of gut microbiota by vancomycin and bacitracin improves insulin resistance via glucagon‐like peptide 1 in diet‐induced obesity. The FASEB Journal. 29(6). 2397–2411. 171 indexed citations
8.
Lee, Young Sup, Hyeonsoo Jeong, Mengistie Taye, et al.. (2015). Genome-wide Association Study (GWAS) and Its Application for Improving the Genomic Estimated Breeding Values (GEBV) of the Berkshire Pork Quality Traits. Asian-Australasian Journal of Animal Sciences. 28(11). 1551–1557. 3 indexed citations
9.
Choe, Sung Sik, Kyung Cheul Shin, Sojeong Ka, et al.. (2014). Macrophage HIF-2α Ameliorates Adipose Tissue Inflammation and Insulin Resistance in Obesity. Diabetes. 63(10). 3359–3371. 84 indexed citations
10.
Son, You Hwa, Sojeong Ka, Ayoung Kim, & Jae Bum Kim. (2014). Regulation of Adipocyte Differentiation via MicroRNAs. Endocrinology and Metabolism. 29(2). 122–122. 89 indexed citations
11.
Ka, Sojeong, Ellen Markljung, Frank W. Albert, et al.. (2013). Expression of carnitine palmitoyl-CoA transferase-1B is influenced by a cis-acting eQTL in two chicken lines selected for high and low body weight. Physiological Genomics. 45(9). 367–376. 12 indexed citations
12.
Ka, Sojeong, Frank W. Albert, D. Michael Denbow, et al.. (2011). Differentially expressed genes in hypothalamus in relation to genomic regions under selection in two chicken lines resulting from divergent selection for high or low body weight. Neurogenetics. 12(3). 211–221. 13 indexed citations
13.
Rubin, Carl‐Johan, Michael C. Zody, Jonas Eriksson, et al.. (2010). Whole-genome resequencing reveals loci under selection during chicken domestication. Nature. 464(7288). 587–591. 761 indexed citations breakdown →
14.
Ka, Sojeong. (2009). Gene Expression in the Brains of Two Lines of Chicken Divergently Selected for High and Low Body Weight. KTH Publication Database DiVA (KTH Royal Institute of Technology). 1 indexed citations
16.
Apostolova, Galina, Sojeong Ka, Finn Hallböök, et al.. (2007). Neurotransmitter phenotype-specific expression changes in developing sympathetic neurons. Molecular and Cellular Neuroscience. 35(3). 397–408. 15 indexed citations
17.
Ka, Sojeong, Carolyn Fitzsimmons, Lina Jacobsson, et al.. (2005). Expression Analysis of Growth and Energy Regulation‐Associated Genes in Two Divergent Chicken Strains. Annals of the New York Academy of Sciences. 1040(1). 357–359. 5 indexed citations
18.
Hong, Sunghoi, et al.. (2003). p80 coilin, a coiled body-specific protein, interacts with ataxin-1, the SCA1 gene product. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease. 1638(1). 35–42. 20 indexed citations
19.
Hong, Sunghoi, et al.. (2002). USP7, a Ubiquitin-Specific Protease, Interacts with Ataxin-1, the SCA1 Gene Product. Molecular and Cellular Neuroscience. 20(2). 298–306. 53 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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