Kevin Song

9.9k citations
181 papers · 2.6k indexed · h-index 26
Topics
Multiple Myeloma Research and Treatments (90 papers)Protein Degradation and Inhibitors (47 papers)Cancer Treatment and Pharmacology (38 papers)
Journals
Nature CommunicationsJournal of Clinical OncologySHILAP Revista de lepidopterología

In The Last Decade

Kevin Song

162 papers receiving 2.6k citations

Peers

Kevin Song
Comparison fields: 5 of 108
  • Hematology 1.4k
  • Oncology 1.1k
  • Molecular Biology 1.1k
  • Pathology and Forensic Medicine 637
  • Genetics 565
Replace Sung‐Eun Lee with:
Sung‐Eun Lee South Korea
Argiris Symeonidis Greece
Michael Keeney Canada
Chan‐Jeoung Park South Korea
Mohamed E. Salama United States
Meral Beksaç Türkiye
Lucía López‐Corral Spain
Francesca Bonifazi Italy
Ulf‐Henrik Mellqvist Sweden
Christoph Röllig Germany
Kevin Song relative to Sung‐Eun Lee South Korea Sung‐Eun Lee's profile →
Citations per field
00.5×2.7×
Sung‐Eun Lee · 1×
Citations per year

Countries citing papers authored by Kevin Song

Since Specialization
Citations

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

Fields of papers citing papers by Kevin Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kevin Song

This figure shows the co-authorship network connecting the top 25 collaborators of Kevin Song. A scholar is included among the top collaborators of Kevin Song 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 Kevin Song. Kevin Song 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 1
3 2
4 2
5 1
6 1
7 1
8 6
9 1
10 2
11 11
12 42
13 1
14 0
15 1
16
HEALTH RESOURCE UTILIZATION WITH CONTINUOUS LENALIDOMIDE TREATMENT (TX) IN ELDERLY PATIENTS WITH NEWLY DIAGNOSED MULTIPLE MYELOMA (NDMM)
1
17 3
18 39
19 39
20 78

About Kevin Song

Kevin Song is a scholar working on Hematology, Oncology and Genetics, having authored 181 papers that have together received 2.6k indexed citations. Recurring topics across this work include Multiple Myeloma Research and Treatments (90 papers), Protein Degradation and Inhibitors (47 papers) and Cancer Treatment and Pharmacology (38 papers). The work is most often cited by research in Hematology (1.4k citations), Genetics (565 citations) and Oncology (1.1k citations). Kevin Song has collaborated with scholars based in Canada, United States and France. Frequent co-authors include Donna E. Hogge, Donna L. Forrest, Cynthia L. Toze, John D. Shepherd, Thomas J. Nevill, Nelson Leung, Michael J. Barnett, Heather J. Sutherland, Joseph M. Connors and Stephen H. Nantel. Their work appears in journals such as Nature Communications, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

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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