Jun Matsubayashi

3.0k total citations
125 papers, 2.0k citations indexed

About

Jun Matsubayashi is a scholar working on Pulmonary and Respiratory Medicine, Surgery and Oncology. According to data from OpenAlex, Jun Matsubayashi has authored 125 papers receiving a total of 2.0k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Pulmonary and Respiratory Medicine, 40 papers in Surgery and 39 papers in Oncology. Recurrent topics in Jun Matsubayashi's work include Lung Cancer Diagnosis and Treatment (29 papers), Lung Cancer Treatments and Mutations (27 papers) and Radiomics and Machine Learning in Medical Imaging (15 papers). Jun Matsubayashi is often cited by papers focused on Lung Cancer Diagnosis and Treatment (29 papers), Lung Cancer Treatments and Mutations (27 papers) and Radiomics and Machine Learning in Medical Imaging (15 papers). Jun Matsubayashi collaborates with scholars based in Japan, United States and United Kingdom. Jun Matsubayashi's co-authors include Toshitaka Nagao, Norihiko Ikeda, Tatsuo Ohira, Yoshihisa Shimada, Masatoshi Kakihana, Naohiro Kajiwara, Hisashi Saji, Yasufumi Kato, Yujin Kudo and Koichi Yoshida and has published in prestigious journals such as Journal of Clinical Investigation, Journal of Clinical Oncology and SHILAP Revista de lepidopterología.

In The Last Decade

Jun Matsubayashi

111 papers receiving 2.0k citations

Peers

Jun Matsubayashi
Comparison fields: 5 of 97
  • Pulmonary and Respiratory Medicine 971
  • Oncology 755
  • Surgery 598
  • Radiology, Nuclear Medicine and Imaging 329
  • Molecular Biology 304
Replace Sara E. Monaco with:
Sara E. Monaco United States
Masaharu Hata Japan
Fabien Forest France
Supriya Mallick India
Odile Casiraghi France
Han‐Sin Jeong South Korea
Jocelyn Migliacci United States
Licia Laurino Italy
Akiko Miyagi Maeshima Japan
P N Plowman United Kingdom
Sara E. Monaco United States View profile →
Citations per field, relative to Jun Matsubayashi
Jun Matsubayashi · 1×
Citations per year, relative to Jun Matsubayashi
Jun Matsubayashi · 1×

Countries citing papers authored by Jun Matsubayashi

Since Specialization
Citations

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

Fields of papers citing papers by Jun Matsubayashi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jun Matsubayashi

This figure shows the co-authorship network connecting the top 25 collaborators of Jun Matsubayashi. A scholar is included among the top collaborators of Jun Matsubayashi 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 Jun Matsubayashi. Jun Matsubayashi 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
# Work Indexed citations
1 0
2 0
3 4
4 6
5 0
6 4
7 9
8 10
9 8
10 21
11 0
12 51
13 16
14 55
15 26
16 12
17 29
18 1
19
Expression of G-protein-coupled receptor kinase 4 is associated with breast cancer tumorigenesis
1
20 14

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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