Tsukasa Nakamura

1.9k total citations · 1 hit paper
18 papers, 839 citations indexed

About

Tsukasa Nakamura is a scholar working on Molecular Biology, Materials Chemistry and Nephrology. According to data from OpenAlex, Tsukasa Nakamura has authored 18 papers receiving a total of 839 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 7 papers in Materials Chemistry and 3 papers in Nephrology. Recurrent topics in Tsukasa Nakamura's work include Enzyme Structure and Function (7 papers), Protein Structure and Dynamics (6 papers) and Genomics and Phylogenetic Studies (3 papers). Tsukasa Nakamura is often cited by papers focused on Enzyme Structure and Function (7 papers), Protein Structure and Dynamics (6 papers) and Genomics and Phylogenetic Studies (3 papers). Tsukasa Nakamura collaborates with scholars based in Japan, United States and Mexico. Tsukasa Nakamura's co-authors include Kentaro Tomii, Kazunori Yamada, Kazutaka Katoh, Isao Ebihara, Chifuyu Ushiyama, Noriaki Shimada, Shingo Suzuki, Hikaru Koide, Daisuke Kihara and Masanori Hara and has published in prestigious journals such as Nature Communications, Bioinformatics and Journal of Molecular Biology.

In The Last Decade

Tsukasa Nakamura

17 papers receiving 832 citations

Hit Papers

Parallelization of MAFFT for large-scale multiple sequenc... 2018 2026 2020 2023 2018 200 400 600
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Parallelization of MAFFT for large-scale multiple sequence alignments
    2018 628 citations Tsukasa Nakamura, Kazunori Yamada et al. Bioinformatics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Tsukasa Nakamura Japan 7 435 169 137 130 90 18 839
Noború Fujihara Japan 18 337 0.8× 64 0.4× 331 2.4× 102 0.8× 94 1.0× 127 1.1k
Joshua G Vandersteen United States 5 923 2.1× 251 1.5× 270 2.0× 210 1.6× 111 1.2× 5 1.6k
Béatrice Segurens France 20 1.2k 2.8× 435 2.6× 465 3.4× 390 3.0× 81 0.9× 26 2.1k
Francesco Vezzi Sweden 17 527 1.2× 233 1.4× 215 1.6× 125 1.0× 39 0.4× 26 944
Nicolas Krezdorn Germany 17 295 0.7× 349 2.1× 85 0.6× 62 0.5× 21 0.2× 32 789
Guido Krupp Germany 27 1.5k 3.5× 399 2.4× 237 1.7× 242 1.9× 43 0.5× 62 2.1k
Lydia Steiner Germany 9 983 2.3× 192 1.1× 154 1.1× 283 2.2× 62 0.7× 9 1.4k

Countries citing papers authored by Tsukasa Nakamura

Since Specialization
Citations

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

Fields of papers citing papers by Tsukasa Nakamura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tsukasa Nakamura

This figure shows the co-authorship network connecting the top 25 collaborators of Tsukasa Nakamura. A scholar is included among the top collaborators of Tsukasa Nakamura 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 Tsukasa Nakamura. Tsukasa Nakamura is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Kagaya, Yuki, Zicong Zhang, Nabil Ibtehaz, et al.. (2025). NuFold: end-to-end approach for RNA tertiary structure prediction with flexible nucleobase center representation. Nature Communications. 16(1). 881–881. 13 indexed citations
2.
Bekker, Gert‐Jan, Chioko Nagao, Matsuyuki Shirota, et al.. (2025). Protein Data Bank Japan: Improved tools for sequence‐oriented analysis of protein structures. Protein Science. 34(3). e70052–e70052. 4 indexed citations
3.
Bekker, Gert‐Jan, Chioko Nagao, Matsuyuki Shirota, et al.. (2025). Protein Data Bank Japan: Computational Resources for Analysis of Protein Structures. Journal of Molecular Biology. 437(15). 169013–169013. 5 indexed citations
4.
Han, Zhu, et al.. (2025). AI-based quality assessment methods for protein structure models from cryo-EM. PubMed. 9. 100164–100164. 2 indexed citations
5.
Terashi, Genki, et al.. (2023). DeepMainmast: integrated protocol of protein structure modeling for cryo-EM with deep learning and structure prediction. Nature Methods. 21(1). 122–131. 27 indexed citations
6.
Yamada, Kazunori, et al.. (2020). Developing a Novel Recurrent Neural Network Architecture with Fewer Parameters and Good Learning Performance. Interdisciplinary Information Sciences. 27(1). 25–40. 2 indexed citations
7.
Nakamura, Tsukasa, Kazunori Yamada, Kentaro Tomii, & Kazutaka Katoh. (2018). Parallelization of MAFFT for large-scale multiple sequence alignments. Bioinformatics. 34(14). 2490–2492. 628 indexed citations breakdown →
8.
Nakamura, Tsukasa, Toshiyuki Oda, Yoshinori Fukasawa, & Kentaro Tomii. (2017). Template‐based quaternary structure prediction of proteins using enhanced profile–profile alignments. Proteins Structure Function and Bioinformatics. 86(S1). 274–282. 8 indexed citations
9.
Nakamura, Tsukasa & Kentaro Tomii. (2016). Effects of the difference in similarity measures on the comparison of ligand-binding pockets using a reduced vector representation of pockets. Biophysics and Physicobiology. 13(0). 139–147. 3 indexed citations
10.
11.
Nakamura, Tsukasa, et al.. (2015). Scene recognition based on gradient feature for autonomous mobile robot and its FPGA implementation. 7. 1–4. 1 indexed citations
12.
Nakamura, Tsukasa, et al.. (2010). A method of obtaining sense of touch by using EEG. 54. 276–281. 1 indexed citations
13.
Nakamura, Tsukasa, et al.. (2009). A Method for Evaluating the Degree of Human's Preference Based on EEG Analysis. 732–735. 3 indexed citations
14.
Angulo, Ofelia, et al.. (2009). USE OF PURCHASE PREFERENCE OPTIONS TO INCREASE “NO PREFERENCE” FREQUENCIES IN PLACEBO PREFERENCE TESTS. Journal of Sensory Studies. 24(2). 258–268. 19 indexed citations
15.
Ohno, Akira, Tsukasa Nakamura, & Jun‐ichi Hanna. (2009). Effect of Dipoles on Charge Carrier Transport in Smectic Liquid Crystal. Molecular Crystals and Liquid Crystals. 510(1). 293/[1427]–299/[1433].
16.
Nakamura, Tsukasa, Chifuyu Ushiyama, Noriaki Shimada, et al.. (2001). Changes in concentrations of type IV collagen and tissue inhibitor of metalloproteinase‐1 in patients with paraquat poisoning. Journal of Applied Toxicology. 21(6). 445–447. 5 indexed citations
17.
Nakamura, Tsukasa, Chifuyu Ushiyama, Shingo Suzuki, et al.. (2000). Elevation of Serum Levels of Metalloproteinase-1, Tissue Inhibitor of Metalloproteinase-1 and Type IV Collagen, and Plasma Levels of Metalloproteinase-9 in Polycystic Kidney Disease. American Journal of Nephrology. 20(1). 32–36. 63 indexed citations
18.
Nakamura, Tsukasa, Chifuyu Ushiyama, Shingo Suzuki, et al.. (2000). The Urinary Podocyte as a Marker for the Differential Diagnosis of Idiopathic Focal Glomerulosclerosis and Minimal-Change Nephrotic Syndrome. American Journal of Nephrology. 20(3). 175–179. 53 indexed citations

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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