Junil Kim

2.1k total citations · 1 hit paper
67 papers, 1.4k citations indexed

About

Junil Kim is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Junil Kim has authored 67 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 45 papers in Molecular Biology, 6 papers in Cancer Research and 5 papers in Oncology. Recurrent topics in Junil Kim's work include Gene Regulatory Network Analysis (14 papers), Single-cell and spatial transcriptomics (13 papers) and Bioinformatics and Genomic Networks (9 papers). Junil Kim is often cited by papers focused on Gene Regulatory Network Analysis (14 papers), Single-cell and spatial transcriptomics (13 papers) and Bioinformatics and Genomic Networks (9 papers). Junil Kim collaborates with scholars based in South Korea, United States and Denmark. Junil Kim's co-authors include Kwang‐Hyun Cho, Kyoung‐Jae Won, Jeong‐Rae Kim, Hong‐Duk Youn, Eun‐Jung Cho, Sang‐Min Park, Seong‐Tae Kim, Kyunghwan Kim, Hye-Rim Lee and Sun-Ju Yi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nucleic Acids Research and Nature Communications.

In The Last Decade

Junil Kim

62 papers receiving 1.4k citations

Hit Papers

Advances in single-cell omics and multiomics for high-res... 2024 2026 2025 2024 10 20 30 40 50

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Junil Kim South Korea 22 1.0k 178 109 105 89 67 1.4k
Efthymios Kyrodimos Greece 15 420 0.4× 214 1.2× 252 2.3× 32 0.3× 91 1.0× 85 1.1k
Xincheng Liu China 21 669 0.7× 392 2.2× 159 1.5× 120 1.1× 56 0.6× 68 1.2k
Xiaolan Yu China 20 462 0.5× 228 1.3× 126 1.2× 81 0.8× 22 0.2× 72 1.2k
Ravi Ramesh Pathak United States 17 595 0.6× 90 0.5× 162 1.5× 59 0.6× 57 0.6× 41 1.1k
Monika Ray United States 7 1.3k 1.3× 196 1.1× 183 1.7× 192 1.8× 77 0.9× 21 1.8k
Jaeyoon Kim South Korea 22 663 0.7× 118 0.7× 95 0.9× 87 0.8× 74 0.8× 60 1.4k
Hsiuying Wang Taiwan 21 454 0.5× 243 1.4× 53 0.5× 49 0.5× 47 0.5× 84 1.2k
Yao Yao China 22 1.1k 1.1× 286 1.6× 229 2.1× 63 0.6× 96 1.1× 105 2.0k
Ming Qi China 17 909 0.9× 90 0.5× 129 1.2× 353 3.4× 47 0.5× 84 1.8k
Xudong Shi United States 31 1.1k 1.1× 161 0.9× 142 1.3× 67 0.6× 210 2.4× 78 2.1k

Countries citing papers authored by Junil Kim

Since Specialization
Citations

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

Fields of papers citing papers by Junil Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junil Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Junil Kim. A scholar is included among the top collaborators of Junil Kim 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 Junil Kim. Junil Kim 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.
Shin, Seung Yong, Ji‐Seon Ahn, Junil Kim, et al.. (2025). Multiomics insights into functional constipation: Exploring microbiome, metabolome, and lipidome independent of transit time. Digestive and Liver Disease. 57(10). 1927–1937.
2.
Kim, Junil, et al.. (2025). Single-cell RNA-seq analysis reveals the multi-step process of cellular senescence. Biochemistry and Biophysics Reports. 42. 102042–102042. 1 indexed citations
3.
Hwang, Wonchan, Junil Kim, Shin‐Yeong Kim, et al.. (2024). Unveiling olivine cathodes for high energy-density lithium-ion batteries: a comprehensive review from the atomic level to the electrode scale. Journal of Materials Chemistry A. 12(41). 27800–27824. 6 indexed citations
4.
Kim, Junil, et al.. (2024). Application of computational algorithms for single-cell RNA-seq and ATAC-seq in neurodegenerative diseases. Briefings in Functional Genomics. 24. 1 indexed citations
5.
Choi, Seong Hee, Jeonghwan Kim, Wha Ja Cho, et al.. (2024). DRG2 is required for surface localization of PD-L1 and the efficacy of anti-PD-1 therapy. Cell Death Discovery. 10(1). 260–260. 5 indexed citations
6.
Kim, Junil, Michaela Rothová, Esha Madan, et al.. (2023). Neighbor-specific gene expression revealed from physically interacting cells during mouse embryonic development. Proceedings of the National Academy of Sciences. 120(2). e2205371120–e2205371120. 9 indexed citations
7.
Kim, Hyobin, Amit Kumar, Cecilia Lövkvist, et al.. (2023). CellNeighborEX : deciphering neighbor‐dependent gene expression from spatial transcriptomics data. Molecular Systems Biology. 19(11). e11670–e11670. 15 indexed citations
8.
Raum, Jeffrey C., Junil Kim, Juxiang Yang, et al.. (2022). A PDX1 cistrome and single-cell transcriptome resource of the developing pancreas. Development. 149(13). 4 indexed citations
9.
Kim, Junil, et al.. (2021). VeTra: a tool for trajectory inference based on RNA velocity. Bioinformatics. 37(20). 3509–3513. 15 indexed citations
10.
Yi, Sun-Ju, Hye‐Jung Kim, Kyubin Lee, et al.. (2021). The KDM4B–CCAR1–MED1 axis is a critical regulator of osteoclast differentiation and bone homeostasis. Bone Research. 9(1). 27–27. 27 indexed citations
11.
Kim, Junil, et al.. (2020). TENET: gene network reconstruction using transfer entropy reveals key regulatory factors from single cell transcriptomic data. Nucleic Acids Research. 49(1). e1–e1. 29 indexed citations
12.
Park, Jinah, et al.. (2018). Novel identification of STAT1 as a crucial mediator of ETV6-NTRK3-induced tumorigenesis. Oncogene. 37(17). 2270–2284. 11 indexed citations
13.
Kang, Jin Muk, Sujin Park, Staci Jakyong Kim, et al.. (2015). KIAA1324 Suppresses Gastric Cancer Progression by Inhibiting the Oncoprotein GRP78. Cancer Research. 75(15). 3087–3097. 40 indexed citations
14.
Kim, Junil, Seong‐Jin Kim, & Kazuhito Naka. (2015). Transcriptome sequencing of hematopoietic stem cells and chronic myelgenous leukemia stem cells. Genomics Data. 7. 57–59. 3 indexed citations
15.
Naka, Kazuhito, Kaori Ishihara, Junil Kim, et al.. (2015). Dipeptide species regulate p38MAPK–Smad3 signalling to maintain chronic myelogenous leukaemia stem cells. Nature Communications. 6(1). 8039–8039. 48 indexed citations
16.
Kim, Junil, et al.. (2014). Robustness and Evolvability of the Human Signaling Network. PLoS Computational Biology. 10(7). e1003763–e1003763. 21 indexed citations
17.
Kim, Junil, Sang‐Min Park, & Kwang‐Hyun Cho. (2013). Discovery of a kernel for controlling biomolecular regulatory networks. Scientific Reports. 3(1). 2223–2223. 79 indexed citations
18.
Kim, Junil, et al.. (2011). Locomotion of Paramecium in patterned environments. Bulletin of the American Physical Society. 78.
19.
Kim, Junil, et al.. (2006). The relationship between civil aircraft noise and community annoyance in Korea. Journal of Sound and Vibration. 299(3). 575–586. 39 indexed citations
20.
Kim, Junil, Eun‐Jung Cho, Seong‐Tae Kim, & Hong‐Duk Youn. (2005). CtBP represses p300-mediated transcriptional activation by direct association with its bromodomain. Nature Structural & Molecular Biology. 12(5). 423–428. 102 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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