Hongseok Ha

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

About

Hongseok Ha is a scholar working on Molecular Biology, Plant Science and Genetics. According to data from OpenAlex, Hongseok Ha has authored 52 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Molecular Biology, 22 papers in Plant Science and 14 papers in Genetics. Recurrent topics in Hongseok Ha's work include Chromosomal and Genetic Variations (22 papers), RNA modifications and cancer (15 papers) and RNA and protein synthesis mechanisms (15 papers). Hongseok Ha is often cited by papers focused on Chromosomal and Genetic Variations (22 papers), RNA modifications and cancer (15 papers) and RNA and protein synthesis mechanisms (15 papers). Hongseok Ha collaborates with scholars based in South Korea, United States and United Kingdom. Hongseok Ha's co-authors include Yoon Ki Kim, Sung Ho Boo, Hyun Kyu Song, Yujin Lee, Do Hoon Kwon, Ok Hyun Park, Heui‐Soo Kim, Jinchuan Xing, Jae‐Won Huh and Kung Ahn and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and Molecular Cell.

In The Last Decade

Hongseok Ha

50 papers receiving 1.5k citations

Hit Papers

Endoribonucleolytic Cleav... 2019 2026 2021 2023 2019 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Hongseok Ha South Korea 20 1.3k 428 417 199 72 52 1.5k
Ilona Rafalska Germany 6 1.4k 1.1× 303 0.7× 169 0.4× 91 0.5× 79 1.1× 6 1.6k
Li‐Feng Zhang China 17 1.1k 0.8× 214 0.5× 198 0.5× 488 2.5× 125 1.7× 51 1.4k
Daniel Holoch France 11 1.7k 1.3× 696 1.6× 433 1.0× 153 0.8× 65 0.9× 13 2.0k
Thomas M. Carlile United States 12 1.4k 1.1× 388 0.9× 117 0.3× 50 0.3× 40 0.6× 14 1.6k
Takeshi Haraguchi Japan 15 882 0.7× 501 1.2× 115 0.3× 87 0.4× 191 2.7× 31 1.3k
Pengpeng Liu China 25 1.4k 1.1× 265 0.6× 95 0.2× 281 1.4× 47 0.7× 53 1.6k
Seth W. Cheetham Australia 14 1.1k 0.9× 709 1.7× 160 0.4× 94 0.5× 52 0.7× 26 1.3k
C. Chen United States 18 1.3k 1.1× 104 0.2× 317 0.8× 309 1.6× 174 2.4× 40 1.8k
Rebecca H. Herbst United States 9 1.4k 1.1× 381 0.9× 106 0.3× 173 0.9× 417 5.8× 11 1.9k
Benjamin R. Nelson United States 9 1.8k 1.4× 919 2.1× 51 0.1× 102 0.5× 58 0.8× 12 2.0k

Countries citing papers authored by Hongseok Ha

Since Specialization
Citations

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

Fields of papers citing papers by Hongseok Ha

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hongseok Ha

This figure shows the co-authorship network connecting the top 25 collaborators of Hongseok Ha. A scholar is included among the top collaborators of Hongseok Ha 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 Hongseok Ha. Hongseok Ha 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.
Ha, Hongseok, et al.. (2024). YAP promotes global mRNA translation to fuel oncogenic growth despite starvation. Experimental & Molecular Medicine. 56(10). 2202–2215. 4 indexed citations
2.
Ha, Hongseok, et al.. (2024). Distinct granzyme k expression in immune cells: a single-cell rna-seq meta-analysis. Genes & Genomics. 46(9). 1097–1106.
3.
Boo, Sung Ho, Min Kyung Shin, Hongseok Ha, Jae‐Sung Woo, & Yoon Ki Kim. (2024). Transcriptome-wide analysis for glucocorticoid receptor-mediated mRNA decay reveals various classes of target transcripts. Molecules and Cells. 47(11). 100130–100130. 3 indexed citations
4.
Kim, Jung Min, Hongseok Ha, Joo Mi Yi, et al.. (2023). Human Endogenous Retrovirus-H-Derived miR-4454 Inhibits the Expression of DNAJB4 and SASH1 in Non-Muscle-Invasive Bladder Cancer. Genes. 14(7). 1410–1410. 6 indexed citations
5.
Boo, Sung Ho, Hongseok Ha, Yujin Lee, et al.. (2022). UPF1 promotes rapid degradation of m6A-containing RNAs. Cell Reports. 39(8). 110861–110861. 37 indexed citations
6.
Boo, Sung Ho, Hongseok Ha, & Yoon Ki Kim. (2022). m1A and m6A modifications function cooperatively to facilitate rapid mRNA degradation. Cell Reports. 40(10). 111317–111317. 41 indexed citations
7.
8.
Ha, Hongseok, et al.. (2022). Genomic Analyses of Non-Coding RNAs Overlapping Transposable Elements and Its Implication to Human Diseases. International Journal of Molecular Sciences. 23(16). 8950–8950. 18 indexed citations
9.
Park, Joori, Hyun Jung Hwang, Kwon Jeong, et al.. (2021). The pioneer round of translation ensures proper targeting of ER and mitochondrial proteins. Nucleic Acids Research. 49(21). 12517–12534. 5 indexed citations
10.
Ryu, Incheol, Hongseok Ha, Min Kyung Kim, et al.. (2019). eIF4A3 Phosphorylation by CDKs Affects NMD during the Cell Cycle. Cell Reports. 26(8). 2126–2139.e9. 36 indexed citations
11.
Park, Ok Hyun, Hongseok Ha, Yujin Lee, et al.. (2019). Endoribonucleolytic Cleavage of m6A-Containing RNAs by RNase P/MRP Complex. Molecular Cell. 74(3). 494–507.e8. 427 indexed citations breakdown →
12.
Jeong, Kwon, Incheol Ryu, Joori Park, et al.. (2019). Staufen1 and UPF1 exert opposite actions on the replacement of the nuclear cap-binding complex by eIF4E at the 5′ end of mRNAs. Nucleic Acids Research. 47(17). 9313–9328. 20 indexed citations
13.
Feusier, Julie, W. Scott Watkins, Jainy Thomas, et al.. (2019). Pedigree-based estimation of human mobile element retrotransposition rates. Genome Research. 29(10). 1567–1577. 67 indexed citations
14.
Hu, Tianxiang, Wenhu Pi, Xingguo Zhu, et al.. (2017). Long non-coding RNAs transcribed by ERV-9 LTR retrotransposon act in cis to modulate long-range LTR enhancer function. Nucleic Acids Research. 45(8). gkx055–gkx055. 35 indexed citations
15.
Ha, Hongseok, Nan Wang, & Jinchuan Xing. (2015). Library Construction for High-Throughput Mobile Element Identification and Genotyping. Methods in molecular biology. 1589. 1–15. 3 indexed citations
16.
Gim, Jeong‐An, Hongseok Ha, Kung Ahn, Dae‐Soo Kim, & Heui‐Soo Kim. (2014). Genome-Wide Identification and Classification of MicroRNAs Derived from Repetitive Elements. Genomics & Informatics. 12(4). 261–261. 43 indexed citations
17.
Ha, Hongseok, et al.. (2013). Identification and Promoter Analysis of PERV LTR Subtypes in NIH-Miniature Pig. Molecules and Cells. 35(2). 99–105. 10 indexed citations
18.
Ha, Hongseok, Jae‐Won Huh, Jeong‐An Gim, Kyudong Han, & Heui‐Soo Kim. (2011). Transcriptional variations mediated by an alternative promoter of the FPR3 gene. Mammalian Genome. 22(9-10). 621–633. 6 indexed citations
19.
Kim, Yoon-Ji, Jae‐Won Huh, Dae‐Soo Kim, et al.. (2008). Molecular characterization of the DYX1C1 gene and its application as a cancer biomarker. Journal of Cancer Research and Clinical Oncology. 135(2). 265–270. 13 indexed citations
20.
Huh, Jae‐Won, et al.. (2007). Long Terminal Repeats of Human Endogenous Retrovirus H Family Provide Alternative Polyadenylation Signals to NADSYN1 Gene. 29(3). 395–401. 4 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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