Won‐Min Song

3.3k citations
34 papers · 1.1k · h-index 17

Impact in

  • Neurology top 10%
    • Neuroinflammation and Neurodegeneration Mechanisms
  • Aging top 10%

Papers in

    • Bioinformatics and Genomic Networks 10
    • Gene expression and cancer classification 5
    • RNA Research and Splicing 5
    • Gene Regulatory Network Analysis 5
    • Single-cell and spatial transcriptomics 3
    • RNA modifications and cancer 3

Won‐Min Song

30 papers receiving 1.1k citations

Peers

Won‐Min Song
Comparison fields: 5 of 129
  • Neurology 105
  • Aging 21
  • Hepatology 68
  • Biological Psychiatry 21
  • Molecular Biology 472
Replace Gregory E. Gonye with:
Gregory E. Gonye United States
Venkata Satagopam Luxembourg
Guanghua Xiao United States
Gerald Quon United States
Matan Hofree United States
Xidi Wang China
Anaı̈s Baudot France
Heonjong Han South Korea
Max Kotlyar Canada
Dabin Jeong South Korea
Won‐Min Song relative to Gregory E. Gonye United States Gregory E. Gonye's profile →
Citations per field
00.5×6.3×
Gregory E. Gonye · 1×
Citations per year

Countries citing papers authored by Won‐Min Song

Since Specialization
Citations

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

Fields of papers citing papers by Won‐Min Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Won‐Min Song, 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 Won‐Min Song Line = papers co-authored together Won‐Min Song links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 34 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2015198
2 201289
3 202279
4 201968
5 201566
6 202262
7 201762
8 201761
9 201552
10 202250
11 201943
12 201539
13 201636
14 202131
15 201230
16 201126
17 202317
18 201916
19 201915
20 201614

About Won‐Min Song

Won‐Min Song is a scholar working on Molecular Biology, Computational Theory and Mathematics, Cancer Research, Oncology and Immunology, having authored 34 papers that have together received 1.1k indexed citations. Recurring topics across this work include Bioinformatics and Genomic Networks (10 papers), Gene expression and cancer classification (5 papers), RNA Research and Splicing (5 papers), Gene Regulatory Network Analysis (5 papers), Single-cell and spatial transcriptomics (3 papers), RNA modifications and cancer (3 papers), interferon and immune responses (2 papers) and Neuroinflammation and Neurodegeneration Mechanisms (2 papers). The work is most often cited by research in Neurology (105 citations), Aging (21 citations), Hepatology (68 citations), Biological Psychiatry (21 citations) and Molecular Biology (472 citations). Won‐Min Song has collaborated with scholars based in United States, China and Australia. Frequent co-authors include Bin Zhang, Bin Zhang, Tomaso Aste, Tiziana Di Matteo, Minghui Wang, Zhidong Tu, Xianxiao Zhou, Xiandong Lin, Guo‐Cheng Yuan and Peng Xu. Their work appears in journals such as Nature Communications, Molecular Neurodegeneration, Cancer Research, Alzheimer s & Dementia and iScience.

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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