Sinae Kim

1.4k citations
37 papers · 960 indexed · h-index 16
Topics
Cytokine Signaling Pathways and Interactions (5 papers)Virus-based gene therapy research (3 papers)Bayesian Methods and Mixture Models (3 papers)

In The Last Decade

Sinae Kim

31 papers receiving 937 citations

Peers

Sinae Kim
Comparison fields: 5 of 134
  • Molecular Biology 528
  • Biomedical Engineering 153
  • Artificial Intelligence 116
  • Surgery 108
  • Cancer Research 95
Replace Sergey Mareninov with:
Sergey Mareninov United States
Yingtao Bi United States
Andrea B. Moffitt United States
Bowen Wei China
Antje‐Christine Walz Switzerland
Jani Saarela Finland
Yong Fuga Li United States
Weisheng Zhang China
Paul S. Meltzer United States
Sinae Kim relative to Sergey Mareninov United States Sergey Mareninov's profile →
Citations per field
00.5×8.9×
Sergey Mareninov · 1×
Citations per year

Countries citing papers authored by Sinae Kim

Since Specialization
Citations

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

Fields of papers citing papers by Sinae Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sinae Kim

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 0
2 9
3 0
4 1
5 13
6 52
7 3
8 2
9 5
10 51
11 41
12
An Exploratory Study on Relationship between Local Food’s Brand Equity, Origin Effect and Brand Loyalty in Korea
1
13
A Research on Success Factors of Agricultural Co Brand in Korea
1
14 28
15 32
16 2
17 3
18 45
19 123
20 2

About Sinae Kim

Sinae Kim is a scholar working on Pharmacology, Oncology and Molecular Biology, having authored 37 papers that have together received 960 indexed citations. Recurring topics across this work include Cytokine Signaling Pathways and Interactions (5 papers), Virus-based gene therapy research (3 papers) and Bayesian Methods and Mixture Models (3 papers). The work is most often cited by research in Statistics and Probability (70 citations), Molecular Biology (528 citations) and Genetics (80 citations). Sinae Kim has collaborated with scholars based in South Korea, United States and Singapore. Frequent co-authors include Marina Vannucci, Mahlet G. Tadesse, Hyung‐Min Chung, Hyungwon Choi, Alexey I. Nesvizhskii, Anne‐Claude Gingras, Soo‐Hong Lee, Guoshou Teo, Ben C. Collins and Byung‐Soo Kim. Their work appears in journals such as Bioinformatics, Biomaterials and Biochemistry.

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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