Jun Kato

84 papers receiving 1.1k citations

Peers

Jun Kato
Comparison fields: 5 of 151
  • Human-Computer Interaction 145
  • Health, Toxicology and Mutagenesis 119
  • Computer Science Applications 50
  • Software 33
  • Endocrinology, Diabetes and Metabolism 104
Replace Kenichiro Ishii with:
Kenichiro Ishii Japan
Changqing Zhou China
Minhyeok Lee South Korea
Michael Klein Germany
Costas Papaloukas Greece
Xin Zeng China
Steven J. Rigatti United States
Ying Duan China
Jun Kato relative to Kenichiro Ishii Japan Kenichiro Ishii's profile →
Citations per field
00.5×10×16.7×
Kenichiro Ishii · 1×
Citations per year

Countries citing papers authored by Jun Kato

Since Specialization
Citations

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

Fields of papers citing papers by Jun Kato

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 88 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Platelet-derived growth factor indirectly stimulates angiogenesis in vitro.
1993129
2 2004118
3
Antitumor activity of basic fibroblast growth factor-saporin mitotoxin in vitro and in vivo.
199261
4 200457
5 201347
6 199843
7 199340
8 200637
9 199135
10 201234
11 200934
12 201530
13 201828
14 199226
15 200326
16 202022
17 200520
18
Inorganic polyphosphate stimulates lon-mediated proteolysis of nucleoid proteins in Escherichia coli.
200620
19 201219
20 201318

About Jun Kato

Jun Kato is a scholar working on Computer Vision and Pattern Recognition, Human-Computer Interaction, Molecular Biology, Control and Systems Engineering and Information Systems, having authored 88 papers that have together received 1.2k indexed citations. Recurring topics across this work include Interactive and Immersive Displays (14 papers), Music Technology and Sound Studies (9 papers), Teaching and Learning Programming (6 papers), Parallel Computing and Optimization Techniques (6 papers), Advanced Mathematical Physics Problems (6 papers), Video Analysis and Summarization (5 papers), Multimedia Communication and Technology (5 papers) and Music and Audio Processing (5 papers). The work is most often cited by research in Human-Computer Interaction (145 citations), Health, Toxicology and Mutagenesis (119 citations), Computer Science Applications (50 citations), Software (33 citations) and Endocrinology, Diabetes and Metabolism (104 citations). Jun Kato has collaborated with scholars based in Japan, United States and China. Frequent co-authors include Takeo Igarashi, Masataka Goto, Julie Beitz, A. Raymond Frackelton, Daisuke Sakamoto, Yoko Fujita‐Yamaguchi, Sean McDirmid, Noboru Sato, J. W. Clark and Masahide Yamamoto. Their work appears in journals such as Hepatology Research, PLoS ONE, Scientific Reports, ACM SIGPLAN Notices and Molecular Genetics and Genomics.

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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