Junseok Park

625 total citations · 1 hit paper
10 papers, 435 citations indexed

About

Junseok Park is a scholar working on Molecular Biology, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Junseok Park has authored 10 papers receiving a total of 435 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 3 papers in Artificial Intelligence and 3 papers in Computational Theory and Mathematics. Recurrent topics in Junseok Park's work include Biomedical Text Mining and Ontologies (3 papers), Computational Drug Discovery Methods (3 papers) and Semantic Web and Ontologies (2 papers). Junseok Park is often cited by papers focused on Biomedical Text Mining and Ontologies (3 papers), Computational Drug Discovery Methods (3 papers) and Semantic Web and Ontologies (2 papers). Junseok Park collaborates with scholars based in South Korea, United Kingdom and Sweden. Junseok Park's co-authors include Doheon Lee, Sunjae Lee, Jens Nielsen, Saeed Shoaie, Stefania Vaga, Anna Andréasson, Lars Agréus, Lars Engstrand, Gordon Proctor and Gholamreza Bidkhori and has published in prestigious journals such as PLoS ONE, Scientific Reports and Biochemical and Biophysical Research Communications.

In The Last Decade

Junseok Park

10 papers receiving 429 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.

Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals

2020 327 citations Stefania Vaga, Sunjae Lee et al. Scientific Reports profile →

Peers

Junseok Park

MB

BE

CTM

SURGE

PHYSI

Thomas Nowak France

Yijia Chen China

Xiaopeng Xu China

Yizhuo Wang China

Hari K. Somineni United States

Zhifeng Zhou China

Martin Stražar United States

Yingxin Hu China

Ji Hwan Park South Korea

Sirui Zhang China

Thomas Nowak France

Junseok Park

105 ×0.6

MB

21 ×0.4

BE

22 ×0.6

CTM

44 ×1.2

SURGE

40 ×1.1

PHYSI

Citations per year, relative to Junseok Park Junseok Park (= 1×) peers Thomas Nowak

Countries citing papers authored by Junseok Park

Since Specialization

Citations

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

Fields of papers citing papers by Junseok Park

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junseok Park

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

All Works

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10 of 10 papers shown

1.

Vaga, Stefania, Sunjae Lee, Boyang Ji, et al.. (2020). Compositional and functional differences of the mucosal microbiota along the intestine of healthy individuals. Scientific Reports. 10(1). 14977–14977. 327 indexed citations breakdown →

2.

Park, Junseok, et al.. (2020). An interactive retrieval system for clinical trial studies with context-dependent protocol elements. PLoS ONE. 15(9). e0238290–e0238290. 3 indexed citations

3.

Park, Junseok, et al.. (2020). Reliable Data Collection in Participatory Trials to Assess Digital Healthcare Applications. IEEE Access. 8. 79472–79490. 5 indexed citations

4.

Kim, Hoon, et al.. (2019). Malaria Epidemic Prediction Model by Using Twitter Data and Precipitation Volume in Nigeria. Journal of Korea Multimedia Society. 22(5). 588–600. 4 indexed citations

5.

Park, Junseok, et al.. (2019). Concept embedding to measure semantic relatedness for biomedical information ontologies. Journal of Biomedical Informatics. 94. 103182–103182. 17 indexed citations

6.

Yoo, Sunyong, et al.. (2018). In silico profiling of systemic effects of drugs to predict unexpected interactions. Scientific Reports. 8(1). 1612–1612. 14 indexed citations

7.

Park, Junseok, et al.. (2018). CORUS: Blockchain-Based Trustworthy Evaluation System for Efficacy of Healthcare Remedies. 181–184. 13 indexed citations

8.

Park, Junseok, et al.. (2016). Prediction of drugs having opposite effects on disease genes in a directed network. BMC Systems Biology. 10(S1). 2–2. 22 indexed citations

9.

Lee, Sunjae, et al.. (2015). Predicting unintended effects of drugs based on off-target tissue effects. Biochemical and Biophysical Research Communications. 469(3). 399–404. 16 indexed citations

10.

Ahn, Woo Jin, Sangsik Kim, Junseok Park, et al.. (2012). Design of coupled resonators for wireless power transfer to mobile devices using magnetic field shaping. 772–776. 14 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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