Jaejik Kim

500 total citations
21 papers, 318 citations indexed

About

Jaejik Kim is a scholar working on Molecular Biology, Artificial Intelligence and Signal Processing. According to data from OpenAlex, Jaejik Kim has authored 21 papers receiving a total of 318 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Molecular Biology, 8 papers in Artificial Intelligence and 4 papers in Signal Processing. Recurrent topics in Jaejik Kim's work include Gene Regulatory Network Analysis (5 papers), Advanced Clustering Algorithms Research (5 papers) and Bayesian Methods and Mixture Models (4 papers). Jaejik Kim is often cited by papers focused on Gene Regulatory Network Analysis (5 papers), Advanced Clustering Algorithms Research (5 papers) and Bayesian Methods and Mixture Models (4 papers). Jaejik Kim collaborates with scholars based in United States, South Korea and China. Jaejik Kim's co-authors include Lynne Billard, Shuang Huang, Ji-Young Kim, Yoon‐Goo Kang, Jing Shao, Sungguan Hong, Hyangsoon Noh, Zheng Dong, Han‐Fei Ding and Yong Teng and has published in prestigious journals such as Cancer Research, Oncogene and Expert Systems with Applications.

In The Last Decade

Jaejik Kim

18 papers receiving 309 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jaejik Kim United States	9	169	100	43	39	32	21	318
Chia-Hsin Liu Taiwan	12	166 1.0×	57 0.6×	37 0.9×	27 0.7×	13 0.4×	39	405
Jiayuan Zhong China	12	235 1.4×	58 0.6×	20 0.5×	19 0.5×	7 0.2×	35	358
Milad Mostavi United States	6	221 1.3×	76 0.8×	106 2.5×	18 0.5×	4 0.1×	6	334
Woei-Jyh Lee United States	8	98 0.6×	72 0.7×	40 0.9×	37 0.9×	5 0.2×	14	248
Haiyong Wang China	14	253 1.5×	197 2.0×	27 0.6×	49 1.3×	13 0.4×	26	443
Ying Tan United States	14	87 0.5×	46 0.5×	24 0.6×	55 1.4×	126 3.9×	23	620
Mayetri Gupta United States	11	277 1.6×	51 0.5×	57 1.3×	21 0.5×	19 0.6×	24	457
Giovanna Nicora Italy	9	167 1.0×	57 0.6×	79 1.8×	22 0.6×	7 0.2×	35	434
Yujie You China	8	142 0.8×	40 0.4×	44 1.0×	27 0.7×	4 0.1×	12	389
Ahmed Ghanem United States	11	77 0.5×	23 0.2×	11 0.3×	27 0.7×	9 0.3×	38	338

Countries citing papers authored by Jaejik Kim

Since Specialization

Citations

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

Fields of papers citing papers by Jaejik Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jaejik Kim

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

All Works

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20 of 20 papers shown

Kim, Jaejik, et al.. (2023). A new support vector machine for categorical features. Expert Systems with Applications. 229. 120449–120449. 9 indexed citations

Kim, Jaejik, et al.. (2022). Identification of New Clusters from Labeled Data Using Mixture Models. Journal of Computational Biology. 29(6). 585–596. 1 indexed citations

Noh, Hyangsoon, et al.. (2020). SKA3 overexpression promotes cell proliferation and migration in breast cancer cell lines. Turkish Journal of Biochemistry. 45(6). 681–688. 1 indexed citations

Kim, Jaejik, et al.. (2018). Bootstrapping Time-Course Gene Expression Data for Gene Networks: Application to Gene Relevance Networks. Journal of Computational Biology. 25(12). 1374–1384.

Kim, Jaejik & Lynne Billard. (2018). Double monothetic clustering for histogram-valued data. Communications for Statistical Applications and Methods. 25(3). 263–274. 1 indexed citations

Kim, Jaejik, et al.. (2018). Model validation in dynamic systems for time‐course data with complex error structures. Journal of Chemometrics. 33(3).

Zhang, Dandan, Yue Li, Yue Xu, Jaejik Kim, & Shuang Huang. (2018). Phosphodiesterase 7B/microRNA-200c relationship regulates triple-negative breast cancer cell growth. Oncogene. 38(7). 1106–1120. 33 indexed citations

Kim, Yoonji & Jaejik Kim. (2018). Estimation of Dynamic Systems for Gene Regulatory Networks from Dependent Time-Course Data. Journal of Computational Biology. 25(9). 987–996. 2 indexed citations

Billard, Lynne & Jaejik Kim. (2017). Hierarchical clustering for histogram data. Wiley Interdisciplinary Reviews Computational Statistics. 9(5). 6 indexed citations

10.

Kim, Jaejik. (2016). Validation and selection of ODE models for gene regulatory networks. Chemometrics and Intelligent Laboratory Systems. 157. 104–110. 7 indexed citations

11.

Kim, Jaejik. (2015). Cluster analysis for Seoul apartment price using symbolic data. Journal of the Korean Data and Information Science Society. 26(6). 1239–1247. 2 indexed citations

12.

Noonepalle, Satish, Maria Ouzounova, Jaejik Kim, et al.. (2015). Abstract 4060: Promoter methylation regulates interferon-γ induced indoleamine 2,3-dioxygenase expression in breast cancer. Cancer Research. 75(15_Supplement). 4060–4060. 3 indexed citations

13.

Hong, Sungguan, Hyangsoon Noh, Yong Teng, et al.. (2014). SHOX2 Is a Direct miR-375 Target and a Novel Epithelial-to-Mesenchymal Transition Inducer in Breast Cancer Cells. Neoplasia. 16(4). 279–290.e5. 69 indexed citations

14.

Kim, Jaejik, et al.. (2013). Model Discrimination in Dynamic Molecular Systems: Application to Parotid De-differentiation Network. Journal of Computational Biology. 20(7). 524–539. 4 indexed citations

15.

Shao, Jing, Yong Teng, Ravi N. Padia, et al.. (2013). COP1 and GSK3β Cooperate to Promote c-Jun Degradation and Inhibit Breast Cancer Cell Tumorigenesis. Neoplasia. 15(9). 1075–IN11. 42 indexed citations

16.

Crăciun, Gheorghe, Jaejik Kim, Casian Pantea, & Grzegorz A. Rempała. (2012). Statistical Model for Biochemical Network Inference. Communications in Statistics - Simulation and Computation. 42(1). 121–137. 5 indexed citations

17.

Kim, Jaejik & Lynne Billard. (2012). Dissimilarity Measures for Histogram-valued Observations. Communication in Statistics- Theory and Methods. 42(2). 283–303. 19 indexed citations

18.

Kim, Jaejik & Lynne Billard. (2012). Dissimilarity measures and divisive clustering for symbolic multimodal-valued data. Computational Statistics & Data Analysis. 56(9). 2795–2808. 9 indexed citations

19.

Kang, Yoon‐Goo, et al.. (2011). Release of bisphenol A from resin composite used to bond orthodontic lingual retainers. American Journal of Orthodontics and Dentofacial Orthopedics. 140(6). 779–789. 53 indexed citations

20.

Kim, Jaejik & Lynne Billard. (2011). A polythetic clustering process and cluster validity indexes for histogram-valued objects. Computational Statistics & Data Analysis. 55(7). 2250–2262. 18 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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