Yejin Kim

665 total citations · 1 hit paper
31 papers, 373 citations indexed

About

Yejin Kim is a scholar working on Psychiatry and Mental health, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Yejin Kim has authored 31 papers receiving a total of 373 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Psychiatry and Mental health, 9 papers in Molecular Biology and 8 papers in Artificial Intelligence. Recurrent topics in Yejin Kim's work include Computational Drug Discovery Methods (7 papers), Machine Learning in Healthcare (6 papers) and Dementia and Cognitive Impairment Research (6 papers). Yejin Kim is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Machine Learning in Healthcare (6 papers) and Dementia and Cognitive Impairment Research (6 papers). Yejin Kim collaborates with scholars based in United States, South Korea and Finland. Yejin Kim's co-authors include Xiaoqian Jiang, Jing Tang, Luyao Chen, Ziyi Li, Yizhuo Wang, Shuyu Zheng, Zhao Li, Ajay Jaiswal, Ying Ding and Tianhao Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and Cancer Research.

In The Last Decade

Yejin Kim

26 papers receiving 366 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.

CancerGPT for few shot drug pair synergy prediction using large pretrained language models

2024 59 citations Tianhao Li, Ajay Jaiswal et al. npj Digital Medicine profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yejin Kim United States	10	127	106	89	44	36	31	373
Nicholas C. Firth United Kingdom	10	117 0.9×	115 1.1×	32 0.4×	51 1.2×	100 2.8×	15	429
Jiansheng Wu China	11	261 2.1×	156 1.5×	98 1.1×	36 0.8×	17 0.5×	39	525
Alvaro Ulloa United States	10	247 1.9×	230 2.2×	125 1.4×	64 1.5×	21 0.6×	20	740
Arvind Nongpiur India	6	59 0.5×	74 0.7×	20 0.2×	27 0.6×	17 0.5×	15	259
Xiao Gan United States	6	293 2.3×	193 1.8×	33 0.4×	174 4.0×	13 0.4×	9	720
Michele Fratello Finland	15	328 2.6×	257 2.4×	31 0.3×	95 2.2×	46 1.3×	35	719
Giovanna Maria Dimitri Italy	12	81 0.6×	51 0.5×	94 1.1×	12 0.3×	5 0.1×	42	374
Antoine Lizée United States	7	269 2.1×	145 1.4×	73 0.8×	12 0.3×	26 0.7×	8	541
Petar V. Todorov United States	7	132 1.0×	91 0.9×	18 0.2×	14 0.3×	16 0.4×	10	286
Dario Kringel Germany	12	71 0.6×	43 0.4×	60 0.7×	7 0.2×	26 0.7×	28	412

Countries citing papers authored by Yejin Kim

Since Specialization

Citations

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

Fields of papers citing papers by Yejin Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yejin Kim

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

20 of 20 papers shown

Liu, Xiaozhong, et al.. (2025). Multi agent large language models for biomedical hypothesis generation in drug combination discovery. iScience. 28(12). 113984–113984.

Kim, Yejin, et al.. (2024). Prediction of Mild Cognitive Impairment Status: Pilot Study of Machine Learning Models Based on Longitudinal Data From Fitness Trackers. JMIR Formative Research. 8. e55575–e55575. 1 indexed citations

Ma, Xiaotian, et al.. (2024). Clinical outcome-guided deep temporal clustering for disease progression subtyping. Journal of Biomedical Informatics. 158. 104732–104732. 1 indexed citations

Tariq, Muhammad Bilal, Jaroslaw Aronowski, Yang C. Fann, et al.. (2024). An interpretable framework to identify responsive subgroups from clinical trials regarding treatment effects: Application to treatment of intracerebral hemorrhage. SHILAP Revista de lepidopterología. 3(5). e0000493–e0000493. 1 indexed citations

Li, Tianhao, et al.. (2024). CancerGPT for few shot drug pair synergy prediction using large pretrained language models. npj Digital Medicine. 7(1). 40–40. 59 indexed citations breakdown →

Kim, Yejin, et al.. (2023). Using artificial intelligence to learn optimal regimen plan for Alzheimer’s disease. Journal of the American Medical Informatics Association. 30(10). 1645–1656. 9 indexed citations

Kim, Yejin, et al.. (2023). Deep single-cell RNA-seq data clustering with graph prototypical contrastive learning. Bioinformatics. 39(6). 17 indexed citations

Chen, Luyao, et al.. (2023). Inferring Personalized Treatment Effect of Antihypertensives on Alzheimer’s Disease Using Deep Learning. PubMed. 2023. 49–57.

Kim, Yejin, Kai Zhang, Sean I. Savitz, et al.. (2022). Counterfactual analysis of differential comorbidity risk factors in Alzheimer’s disease and related dementias. SHILAP Revista de lepidopterología. 1(3). e0000018–e0000018. 4 indexed citations

10.

Zhang, Kai, et al.. (2022). Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine. JMIR Medical Informatics. 11. e38266–e38266. 7 indexed citations

11.

Chen, Luyao, et al.. (2022). Emulate randomized clinical trials using heterogeneous treatment effect estimation for personalized treatments: Methodology review and benchmark. Journal of Biomedical Informatics. 137. 104256–104256. 16 indexed citations

12.

Wang, Yinyin, Luyao Chen, Zhongming Zhao, et al.. (2021). Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence. Scientific Reports. 11(1). 23179–23179. 42 indexed citations

13.

Shams, Shayan, Yejin Kim, Ananth Annapragada, et al.. (2021). Population stratification enables modeling effects of reopening policies on mortality and hospitalization rates. Journal of Biomedical Informatics. 119. 103818–103818. 4 indexed citations

14.

Li, Ziyi, Xiaoqian Jiang, Yizhuo Wang, & Yejin Kim. (2021). Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data. Emerging Topics in Life Sciences. 5(6). 765–777. 42 indexed citations

15.

Kim, Yejin, et al.. (2020). Anticancer drug synergy prediction in understudied tissues using transfer learning. Journal of the American Medical Informatics Association. 28(1). 42–51. 61 indexed citations

16.

Zhu, Cong, et al.. (2020). A lightweight convolutional neural network for assessing an EEG risk marker for sudden unexpected death in epilepsy. BMC Medical Informatics and Decision Making. 20(S12). 329–329. 7 indexed citations

17.

Kim, Yejin, Samden Lhatoo, Guo‐Qiang Zhang, Luyao Chen, & Xiaoqian Jiang. (2020). Temporal phenotyping for transitional disease progress: An application to epilepsy and Alzheimer’s disease. Journal of Biomedical Informatics. 107. 103462–103462. 3 indexed citations

18.

Kim, Yejin, et al.. (2020). Automated detection of activity onset after postictal generalized EEG suppression. BMC Medical Informatics and Decision Making. 20(S12). 327–327. 6 indexed citations

19.

Vance, Carroll P., Yejin Kim, Guo‐Qiang Zhang, et al.. (2020). Learning to detect the onset of slow activity after a generalized tonic–clonic seizure. BMC Medical Informatics and Decision Making. 20(S12). 330–330. 3 indexed citations

20.

Kim, Yejin, Robert El‐Kareh, Jimeng Sun, Hwanjo Yu, & Xiaoqian Jiang. (2017). Discriminative and Distinct Phenotyping by Constrained Tensor Factorization. Scientific Reports. 7(1). 1114–1114. 24 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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