Daisuke Kihara

12.3k citations
233 papers · 5.9k indexed · h-index 45
Topics
Protein Structure and Dynamics (124 papers)Enzyme Structure and Function (81 papers)Machine Learning in Bioinformatics (56 papers)

In The Last Decade

Daisuke Kihara

227 papers receiving 5.8k citations

Peers

Daisuke Kihara
Comparison fields: 5 of 170
  • Molecular Biology 4.7k
  • Materials Chemistry 1.7k
  • Computational Theory and Mathematics 1.1k
  • Structural Biology 424
  • Spectroscopy 359
Replace Jianlin Cheng with:
Jianlin Cheng United States
Pablo Chacón Spain
Fei Long United Kingdom
Willy Wriggers United States
Alexey G. Murzin United Kingdom
Sheng‐You Huang China
Peter L. Freddolino United States
Haim J. Wolfson Israel
John Jumper United States
David E. Kim United States
Daisuke Kihara relative to Jianlin Cheng United States Jianlin Cheng's profile →
Citations per field
00.5×1.5×2.1×
Jianlin Cheng · 1×
Citations per year

Countries citing papers authored by Daisuke Kihara

Since Specialization
Citations

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

Fields of papers citing papers by Daisuke Kihara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daisuke Kihara

This figure shows the co-authorship network connecting the top 25 collaborators of Daisuke Kihara. A scholar is included among the top collaborators of Daisuke Kihara 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 Daisuke Kihara. Daisuke Kihara 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
#WorkIndexed citations
1 5
2 0
3 3
4 18
5 9
6 7
7 1
8 22
9 23
10 9
11 26
12 12
13 6
14 13
15 29
16 45
17 78
18 142
19 15
20 23

About Daisuke Kihara

Daisuke Kihara is a scholar working on Structural Biology, Molecular Biology and Computational Theory and Mathematics, having authored 233 papers that have together received 5.9k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (124 papers), Enzyme Structure and Function (81 papers) and Machine Learning in Bioinformatics (56 papers). The work is most often cited by research in Structural Biology (424 citations), Molecular Biology (4.7k citations) and Computational Theory and Mathematics (1.1k citations). Daisuke Kihara has collaborated with scholars based in United States, Japan and South Korea. Frequent co-authors include Jeffrey Skolnick, Lee Sael, Genki Terashi, Troy Hawkins, Juan Esquivel‐Rodríguez, Charles Christoffer, Woong‐Hee Shin, Yi‐Feng Yang, Yang Zhang and Vishwesh Venkatraman. Their work appears in journals such as Nature, Science and Proceedings of the National Academy of Sciences.

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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