Akio Kimura

31 papers receiving 290 citations

Peers

Akio Kimura
Comparison fields: 5 of 69
  • Endocrinology 177
  • Health, Toxicology and Mutagenesis 68
  • Molecular Biology 120
  • Computer Vision and Pattern Recognition 34
  • Biophysics 8
Replace Kuan-Hao Huang with:
Kuan-Hao Huang United States
Hari Kumar Peguda India
Manoj Sharma India
Ge Ren Hong Kong
Bin Rao United States
Qiaoqiao Li China
Jie Liang China
Masayuki Kashima Japan
Nikhil Joshi United States
Akio Kimura relative to Kuan-Hao Huang United States Kuan-Hao Huang's profile →
Citations per field
00.5×1.5×2.1×
Kuan-Hao Huang · 1×
Citations per year

Countries citing papers authored by Akio Kimura

Since Specialization
Citations

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

Fields of papers citing papers by Akio Kimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009108
2 200858
3 201826
4 200517
5 201510
6 20188
7 20038
8 20008
9 20238
10 20047
11 20196
12 19995
13 19985
14 19945
15 20193
16 20193
17 20093
18
A Deep Learning Method to Impute Missing Values and Compress Genome-ide Polymorphism Data in Rice.
20212
19 20212
20 20042

About Akio Kimura

Akio Kimura is a scholar working on Computer Vision and Pattern Recognition, Molecular Biology, Genetics, Computational Mechanics and Control and Systems Engineering, having authored 35 papers that have together received 309 indexed citations. Recurring topics across this work include Image and Object Detection Techniques (5 papers), Image Processing and 3D Reconstruction (5 papers), Legionella and Acanthamoeba research (4 papers), Genetic Mapping and Diversity in Plants and Animals (4 papers), Gene expression and cancer classification (4 papers), Rice Cultivation and Yield Improvement (3 papers), Bacterial biofilms and quorum sensing (3 papers) and Vibrio bacteria research studies (2 papers). The work is most often cited by research in Endocrinology (177 citations), Health, Toxicology and Mutagenesis (68 citations), Molecular Biology (120 citations), Computer Vision and Pattern Recognition (34 citations) and Biophysics (8 citations). Akio Kimura has collaborated with scholars based in Japan, Philippines and United Arab Emirates. Frequent co-authors include Panagiotis Karanis, Yasuhiro Kusuhara, Takashi Watanabe, Chie Nakajima, Yasuhiko Suzuki, Kimiko Tomioka, Hiroshi Miyamoto, Satoru Maruyama, Hiroshi Suzuki and S. Akita. Their work appears in journals such as IEEJ Transactions on Industry Applications, SAE technical papers on CD-ROM/SAE technical paper series, International Journal of Environmental Research and Public Health, IEEE/ACM Transactions on Computational Biology and Bioinformatics and IEEE Transactions on Applied Superconductivity.

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.

Explore authors with similar magnitude of impact