Tetsuya Ono

2.4k citations
102 papers · 2.0k · h-index 24

Impact in

  • Aging top 2%
    • Genetics, Aging, and Longevity in Model Organisms
    • Metal-Catalyzed Oxygenation Mechanisms

Papers in

Tetsuya Ono

101 papers receiving 1.9k citations

Peers

Tetsuya Ono
Comparison fields: 5 of 133
  • Aging 155
  • Inorganic Chemistry 170
  • Electrochemistry 72
  • Bioengineering 63
  • Physiology 260
Replace Lixia Wang with:
Lixia Wang China
Shakeel Ahmed Ansari Saudi Arabia
William Breuer Israel
Prabhat C. Goswami United States
Irina I. Vlasova Russia
Masahiko Yamaguchi Japan
Ivana Celardo United Kingdom
Yujin Zhang China
Jing Yi China
Marcos López United States
Tetsuya Ono relative to Lixia Wang China Lixia Wang's profile →
Citations per field
00.5×3.9×
Lixia Wang · 1×
Citations per year

Countries citing papers authored by Tetsuya Ono

Since Specialization
Citations

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

Fields of papers citing papers by Tetsuya Ono

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 102 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1980336
2 2006128
3 200797
4 197888
5 201581
6 199868
7 199362
8 198355
9 200151
10 200549
11 200048
12 200648
13
DNA-dependent protein kinase activity correlates with Ku70 expression and radiation sensitivity in esophageal cancer cell lines.
200038
14
Loss of heterozygosity at 11p15 in malignant glioma.
199536
15 199635
16 200833
17 200233
18 198431
19 201530
20 200328

About Tetsuya Ono

Tetsuya Ono is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine, Electrical and Electronic Engineering, Organic Chemistry and Oncology, having authored 102 papers that have together received 2.0k indexed citations. Recurring topics across this work include Electrochemical sensors and biosensors (14 papers), Electrochemical Analysis and Applications (12 papers), Analytical Chemistry and Sensors (12 papers), Oxidative Organic Chemistry Reactions (10 papers), Conducting polymers and applications (9 papers), Occupational and environmental lung diseases (8 papers), Polymer Surface Interaction Studies (8 papers) and DNA Repair Mechanisms (5 papers). The work is most often cited by research in Aging (155 citations), Inorganic Chemistry (170 citations), Electrochemistry (72 citations), Bioengineering (63 citations) and Physiology (260 citations). Tetsuya Ono has collaborated with scholars based in Japan, United States and United Kingdom. Frequent co-authors include R. G. Cutler, Richard G. Cutler, Kiyoshi Fujisawa, Yoshitomo Kashiwagi, Kentaro Yoshida, Nicolai Lehnert, Yutaro Shiota, Yoshio Hosoi, Katsuhiko Sato and Michael J. Getz. Their work appears in journals such as Polymers, Electroanalysis, CHEST Journal, International Journal of Cancer and Inorganic Chemistry.

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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