Changdai Gu

1.0k total citations · 1 hit paper
7 papers, 655 citations indexed

About

Changdai Gu is a scholar working on Molecular Biology, Pediatrics, Perinatology and Child Health and Ecology. According to data from OpenAlex, Changdai Gu has authored 7 papers receiving a total of 655 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Molecular Biology, 1 paper in Pediatrics, Perinatology and Child Health and 1 paper in Ecology. Recurrent topics in Changdai Gu's work include Microbial Metabolic Engineering and Bioproduction (2 papers), RNA and protein synthesis mechanisms (2 papers) and Pregnancy and preeclampsia studies (1 paper). Changdai Gu is often cited by papers focused on Microbial Metabolic Engineering and Bioproduction (2 papers), RNA and protein synthesis mechanisms (2 papers) and Pregnancy and preeclampsia studies (1 paper). Changdai Gu collaborates with scholars based in South Korea, Canada and Indonesia. Changdai Gu's co-authors include Sang Yup Lee, Hyun Uk Kim, Gi Bae Kim, Won Jun Kim, Yeji Kim, Jae Yong Ryu, Jae Sung Cho, Tae Hee Han, Seung Min Yoo and Dongsoo Yang and has published in prestigious journals such as Genome biology, Current Opinion in Biotechnology and Biology of Reproduction.

In The Last Decade

Changdai Gu

5 papers receiving 653 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.

Current status and applications of genome-scale metabolic models

2019 491 citations Changdai Gu, Gi Bae Kim et al. Genome biology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Changdai Gu South Korea	5	573	178	50	38	34	7	655
S. P. Sineoky Russia	16	551 1.0×	316 1.8×	57 1.1×	79 2.1×	20 0.6×	65	668
Matthew DeJongh United States	7	1.0k 1.7×	389 2.2×	92 1.8×	83 2.2×	37 1.1×	16	1.1k
Khushal Khambhati India	11	296 0.5×	79 0.4×	57 1.1×	37 1.0×	9 0.3×	24	450
Meike Baumgart Germany	17	743 1.3×	245 1.4×	100 2.0×	190 5.0×	49 1.4×	42	880
Jung-Won Youn Germany	12	643 1.1×	322 1.8×	19 0.4×	93 2.4×	41 1.2×	19	718
Zhengming Zhu China	14	531 0.9×	162 0.9×	33 0.7×	51 1.3×	156 4.6×	39	715
Tat‐Ming Lo Singapore	7	390 0.7×	129 0.7×	50 1.0×	108 2.8×	35 1.0×	8	494
Weimin Tian China	15	697 1.2×	157 0.9×	47 0.9×	31 0.8×	12 0.4×	49	891
Hildegard Watzlawick Germany	15	453 0.8×	172 1.0×	52 1.0×	100 2.6×	42 1.2×	42	705
Maxime Durot France	10	580 1.0×	184 1.0×	77 1.5×	120 3.2×	28 0.8×	10	702

Countries citing papers authored by Changdai Gu

Since Specialization

Citations

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

Fields of papers citing papers by Changdai Gu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Changdai Gu

This figure shows the co-authorship network connecting the top 25 collaborators of Changdai Gu. A scholar is included among the top collaborators of Changdai Gu 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 Changdai Gu. Changdai Gu 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:

7 of 7 papers shown

1.

Park, Yejin, et al.. (2025). Forecasting topic trends of blockchain utilizing topic modeling and deep learning-based time-series prediction on different document types. Journal of Informetrics. 19(2). 101639–101639.

2.

Gu, Changdai, Woo Dae Jang, Kwangseok Oh, & Jae Yong Ryu. (2024). AnoChem: Prediction of chemical structural abnormalities based on machine learning models. Computational and Structural Biotechnology Journal. 23. 2116–2121.

3.

Gu, Changdai, et al.. (2020). Altered expression of ADM and ADM2 by hypoxia regulates migration of trophoblast and HLA-G expression†. Biology of Reproduction. 104(1). 159–169. 13 indexed citations

4.

Gu, Changdai, Gi Bae Kim, Won Jun Kim, Hyun Uk Kim, & Sang Yup Lee. (2019). Current status and applications of genome-scale metabolic models. Genome biology. 20(1). 121–121. 491 indexed citations breakdown →

5.

Kim, Yeji, Changdai Gu, Hyun Uk Kim, & Sang Yup Lee. (2019). Current status of pan-genome analysis for pathogenic bacteria. Current Opinion in Biotechnology. 63. 54–62. 67 indexed citations

6.

Yang, Dongsoo, Seung Min Yoo, Changdai Gu, et al.. (2019). Expanded synthetic small regulatory RNA expression platforms for rapid and multiplex gene expression knockdown. Metabolic Engineering. 54. 180–190. 42 indexed citations

7.

Cho, Jae Sung, Changdai Gu, Tae Hee Han, Jae Yong Ryu, & Sang Yup Lee. (2019). Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring. Current Opinion in Systems Biology. 15. 1–11. 42 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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