Heetak Lee

857 total citations · 1 hit paper
15 papers, 525 citations indexed

About

Heetak Lee is a scholar working on Molecular Biology, Oncology and Pathology and Forensic Medicine. According to data from OpenAlex, Heetak Lee has authored 15 papers receiving a total of 525 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 4 papers in Oncology and 3 papers in Pathology and Forensic Medicine. Recurrent topics in Heetak Lee's work include Bioinformatics and Genomic Networks (5 papers), Genetic factors in colorectal cancer (3 papers) and Cancer Cells and Metastasis (3 papers). Heetak Lee is often cited by papers focused on Bioinformatics and Genomic Networks (5 papers), Genetic factors in colorectal cancer (3 papers) and Cancer Cells and Metastasis (3 papers). Heetak Lee collaborates with scholars based in South Korea, Austria and Netherlands. Heetak Lee's co-authors include Sanguk Kim, Seong Kyu Han, Donghyo Kim, Heehwa G. Son, Seung‐Jae Lee, Jae‐Seong Yang, Dong-Yeop Lee, Kunyoo Shin, Doyeon Ha and Inhae Kim and has published in prestigious journals such as Nature Communications, Scientific Reports and Science Advances.

In The Last Decade

Heetak Lee

13 papers receiving 519 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

OASIS 2: online application for survival analysis 2 with features for the analysis of maximal lifespan and healthspan in aging research

2016 315 citations Seong Kyu Han, Dong-Yeop Lee et al. Oncotarget profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Heetak Lee South Korea	6	230	181	80	65	51	15	525
Linda P. O’Reilly United States	9	320 1.4×	180 1.0×	22 0.3×	90 1.4×	47 0.9×	20	602
Chronis Fatouros Germany	6	478 2.1×	86 0.5×	95 1.2×	98 1.5×	13 0.3×	6	590
Brian M. Wasko United States	17	820 3.6×	215 1.2×	56 0.7×	144 2.2×	40 0.8×	25	1.0k
Min Zheng China	9	581 2.5×	217 1.2×	28 0.3×	139 2.1×	40 0.8×	41	898
Lily I. Jiang United States	15	458 2.0×	141 0.8×	44 0.6×	36 0.6×	73 1.4×	37	914
Mia L. Pras‐Raves Netherlands	18	686 3.0×	75 0.4×	144 1.8×	223 3.4×	54 1.1×	28	1.0k
Mark T. Miedel United States	11	160 0.7×	64 0.4×	35 0.4×	69 1.1×	11 0.2×	26	541
Siobhan Malany United States	17	369 1.6×	40 0.2×	24 0.3×	67 1.0×	20 0.4×	33	646
Chung-Shyan Liu Taiwan	5	181 0.8×	166 0.9×	18 0.2×	56 0.9×	45 0.9×	10	400
Ashwin Sriram Germany	12	404 1.8×	93 0.5×	79 1.0×	96 1.5×	20 0.4×	16	657

Countries citing papers authored by Heetak Lee

Since Specialization

Citations

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

Fields of papers citing papers by Heetak Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Heetak Lee

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

All Works

Sort: Min cites: Since: Top N: Style:

15 of 15 papers shown

Song, Young Hoon, Muneeb Ullah, Heetak Lee, et al.. (2025). Sugar nanocluster adhesive boosts wound healing in diabetic mice. Carbohydrate Polymer Technologies and Applications. 11. 100933–100933. 1 indexed citations

Koo, Bon‐Kyoung, et al.. (2025). Exploring the Panax ginseng Meyer soil metagenome to uncover antagonistic bacteria against ginseng root rot disease. Journal of Ginseng Research. 49(6). 767–775.

Hwang, Soo‐Yeon, Seojeong Park, Yi Liu, et al.. (2025). Targeting the HER2-ELF3-KRAS axis: a novel therapeutic strategy for KRASG13D colorectal cancer. Molecular Cancer. 24(1). 139–139. 1 indexed citations

Lee, Heetak, Ji‐Hyun Lee, So‐Yeon Park, et al.. (2024). Red2Flpe-SCON: a versatile, multicolor strategy for generating mosaic conditional knockout mice. Nature Communications. 15(1). 4963–4963.

Lee, Heetak, Jihoon Kim, Bon‐Kyoung Koo, et al.. (2024). Transcriptomic Analysis of Air–Liquid Interface Culture in Human Lung Organoids Reveals Regulators of Epithelial Differentiation. Cells. 13(23). 1991–1991. 4 indexed citations

Colozza, Gabriele, Heetak Lee, Alessandra Merenda, et al.. (2023). Intestinal Paneth cell differentiation relies on asymmetric regulation of Wnt signaling by Daam1/2. Science Advances. 9(47). eadh9673–eadh9673. 7 indexed citations

Kim, Jinho, Hyun Jung Kim, Min-Seok Lee, et al.. (2023). Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients. Journal of Translational Medicine. 21(1). 209–209. 7 indexed citations

Lee, Heetak, Gabriele Colozza, Ji‐Hyun Lee, et al.. (2022). SCON—a Short Conditional intrON for conditional knockout with one-step zygote injection. Experimental & Molecular Medicine. 54(12). 2188–2199. 3 indexed citations

Lee, Heetak, Donghyo Kim, Seong Kyu Han, et al.. (2020). Network-based machine learning in colorectal and bladder organoid models predicts anti-cancer drug efficacy in patients. Nature Communications. 11(1). 5485–5485. 148 indexed citations

10.

Lee, Heetak, et al.. (2020). Domain-mediated interactions for protein subfamily identification. Scientific Reports. 10(1). 264–264. 2 indexed citations

11.

Kim, Inhae, et al.. (2019). Link clustering explains non-central and contextually essential genes in protein interaction networks. Scientific Reports. 9(1). 11672–11672. 2 indexed citations

12.

Han, Seong Kyu, Donghyo Kim, Heetak Lee, Inhae Kim, & Sanguk Kim. (2018). Divergence of Noncoding Regulatory Elements Explains Gene–Phenotype Differences between Human and Mouse Orthologous Genes. Molecular Biology and Evolution. 35(7). 1653–1667. 15 indexed citations

13.

Kim, Hyoung Kyu, Heetak Lee, Seung Hun Jeong, et al.. (2017). Energy metabolism and whole-exome sequencing-based analysis of Sasang constitution: a pilot study. Integrative Medicine Research. 6(2). 165–178. 5 indexed citations

14.

Han, Seong Kyu, Dong-Yeop Lee, Heetak Lee, et al.. (2016). OASIS 2: online application for survival analysis 2 with features for the analysis of maximal lifespan and healthspan in aging research. Oncotarget. 7(35). 56147–56152. 315 indexed citations breakdown →

15.

Kim, Inhae, Heetak Lee, Seong Kyu Han, & Sanguk Kim. (2014). Linear Motif-Mediated Interactions Have Contributed to the Evolution of Modularity in Complex Protein Interaction Networks. PLoS Computational Biology. 10(10). e1003881–e1003881. 15 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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