Sunkyu Kim

13.8k citations
76 papers · 7.8k indexed · 4 hit papers · h-index 32

Impact in

Papers in

Sunkyu Kim

68 papers receiving 7.5k citations

Hit Papers

BioBERT: a pre-trained biomedical language representation model for biomedical text mining 2019 · 3.6k citations
3.6k200020262008201710002.0k3.0k

Peers

Sunkyu Kim
Comparison fields: 5 of 186
  • Health Informatics 315
  • Artificial Intelligence 3.0k
  • Cancer Research 1.1k
  • Molecular Biology 4.0k
  • Oncology 1.2k
Replace Kurt Zatloukal with:
Kurt Zatloukal Austria
Alex Zhavoronkov United States
Olga G. Troyanskaya United States
Sampo Pyysalo United Kingdom
Nguyen Quoc Khanh Le Taiwan
George Lee United States
Igor Jurišica Canada
Jakob Nikolas Kather Germany
Tatsuhiko Tsunoda Japan
Casey S. Greene United States
Sunkyu Kim relative to Kurt Zatloukal Austria Kurt Zatloukal's profile →
Citations per field
00.5×3.7×
Kurt Zatloukal · 1×
Citations per year

Countries citing papers authored by Sunkyu Kim

Since Specialization
Citations

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

Fields of papers citing papers by Sunkyu Kim

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Sunkyu Kim, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Sunkyu Kim Line = papers co-authored together Sunkyu Kim links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202436
2 20240
3 20242
4 20233
5 20222
6 201911
7 20181
8 201832
9 201657
10 201667
11 201498
12 2013308
13
An F876L Mutation in Androgen Receptor Confers Genetic and Phenotypic Resistance to MDV3100 (Enzalutamide)
Hit paper breakdown →
2013425
14 2006104
15
Application of Decision Tree for the Classification of Antimicrobial Peptide
20042
16 200322
17 200260
18 200111
19
Deregulation of Glucose Transporter 1 and Glycolytic Gene Expression by c-Myc
Hit paper breakdown →
2000702
20 1999316

About Sunkyu Kim

Sunkyu Kim is a scholar working on Health Informatics, Microbiology, Aging, Genetics and Oncology, having authored 76 papers that have together received 7.8k indexed citations. Recurring topics across this work include Advanced Breast Cancer Therapies (7 papers), Biomedical Text Mining and Ontologies (7 papers), Topic Modeling (6 papers), Cancer-related Molecular Pathways (6 papers), Antimicrobial Peptides and Activities (5 papers), Computational Drug Discovery Methods (5 papers), RNA modifications and cancer (5 papers) and Bioinformatics and Genomic Networks (5 papers). The work is most often cited by research in Health Informatics (315 citations), Artificial Intelligence (3.0k citations), Cancer Research (1.1k citations), Molecular Biology (4.0k citations) and Oncology (1.2k citations). Sunkyu Kim has collaborated with scholars based in South Korea, United States and Switzerland. Frequent co-authors include Jaewoo Kang, Donghyeon Kim, Jinhyuk Lee, Sungdong Kim, Wonjin Yoon, Chi V. Dang, Qing Li, Linda A. Lee, Diane R. Wonsey and Hyunsuk Shim. Their work appears in journals such as Bioinformatics, Blood, Journal of Biological Chemistry, Cancer Research and Clinical Cancer Research.

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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