Yuki Kagaya

1.0k total citations
19 papers, 489 citations indexed

About

Yuki Kagaya is a scholar working on Molecular Biology, Materials Chemistry and Ecology. According to data from OpenAlex, Yuki Kagaya has authored 19 papers receiving a total of 489 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 4 papers in Materials Chemistry and 3 papers in Ecology. Recurrent topics in Yuki Kagaya's work include Machine Learning in Bioinformatics (9 papers), Protein Structure and Dynamics (9 papers) and Bioinformatics and Genomic Networks (5 papers). Yuki Kagaya is often cited by papers focused on Machine Learning in Bioinformatics (9 papers), Protein Structure and Dynamics (9 papers) and Bioinformatics and Genomic Networks (5 papers). Yuki Kagaya collaborates with scholars based in United States, Japan and Vietnam. Yuki Kagaya's co-authors include Kengo Kinoshita, Takeshi Obayashi, Yuichi Aoki, Shu Tadaka, Daisuke Kihara, Genki Terashi, Charles Christoffer, Aashish Jain, Zicong Zhang and Nabil Ibtehaz and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and SHILAP Revista de lepidopterología.

In The Last Decade

Yuki Kagaya

18 papers receiving 488 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Yuki Kagaya United States	9	382	162	40	37	24	19	489
Antonio Chaves-Sanjuán Italy	13	414 1.1×	244 1.5×	20 0.5×	36 1.0×	15 0.6×	34	597
Yuyong Tao China	11	401 1.0×	202 1.2×	26 0.7×	38 1.0×	10 0.4×	18	615
Fulei Nie China	13	515 1.3×	154 1.0×	15 0.4×	22 0.6×	57 2.4×	18	615
Michelle Hooi Australia	14	382 1.0×	60 0.4×	67 1.7×	46 1.2×	7 0.3×	14	484
Małgorzata Gutkowska Poland	12	492 1.3×	163 1.0×	48 1.2×	19 0.5×	24 1.0×	18	576
Eli J. Draizen United States	6	288 0.8×	28 0.2×	38 0.9×	44 1.2×	40 1.7×	10	349
Robert Ietswaart United States	10	338 0.9×	176 1.1×	11 0.3×	50 1.4×	43 1.8×	13	449
Julien Henri France	12	355 0.9×	31 0.2×	50 1.3×	16 0.4×	23 1.0×	31	443
Ariel Erijman Israel	9	392 1.0×	30 0.2×	36 0.9×	35 0.9×	19 0.8×	11	465
Craig W. Gambogi United States	6	242 0.6×	85 0.5×	20 0.5×	41 1.1×	31 1.3×	10	283

Countries citing papers authored by Yuki Kagaya

Since Specialization

Citations

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

Fields of papers citing papers by Yuki Kagaya

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuki Kagaya

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

All Works

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19 of 19 papers shown

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

Kagaya, Yuki, et al.. (2025). Distance-AF improves predicted protein structure models by AlphaFold2 with user-specified distance constraints. Communications Biology. 8(1). 1392–1392.

Christoffer, Charles, Yuki Kagaya, Jacob Verburgt, et al.. (2025). Integrative Protein Assembly With LZerD and Deep Learning in CAPRI 47–55. Proteins Structure Function and Bioinformatics. 2 indexed citations

Verburgt, Jacob, et al.. (2025). Learning with Privileged Knowledge Distillation for Improved Peptide–Protein Docking. ACS Omega. 10(25). 26684–26693. 2 indexed citations

Kagaya, Yuki, Tsukasa Nakamura, Jacob Verburgt, et al.. (2025). Structure Modeling Protocols for Protein Multimer and RNA in CASP16 With Enhanced MSAs , Model Ranking, and Deep Learning. Proteins Structure Function and Bioinformatics. 94(1). 167–182. 2 indexed citations

Lee, Andy, Cataixa López, Yuki Kagaya, et al.. (2024). Genetic adaptation despite high gene flow in a range‐expanding population. Molecular Ecology. 34(15). e17511–e17511. 1 indexed citations

Ibtehaz, Nabil, Yuki Kagaya, & Daisuke Kihara. (2023). Domain-PFP allows protein function prediction using function-aware domain embedding representations. Communications Biology. 6(1). 1103–1103. 11 indexed citations

Obayashi, Takeshi, et al.. (2022). COXPRESdb v8: an animal gene coexpression database navigating from a global view to detailed investigations. Nucleic Acids Research. 51(D1). D80–D87. 17 indexed citations

Aderinwale, Tunde, Charles Christoffer, Genki Terashi, et al.. (2022). Real-time structure search and structure classification for AlphaFold protein models. Communications Biology. 5(1). 316–316. 41 indexed citations

10.

Obayashi, Takeshi, et al.. (2022). ATTED-II v11: A Plant Gene Coexpression Database Using a Sample Balancing Technique by Subagging of Principal Components. Plant and Cell Physiology. 63(6). 869–881. 64 indexed citations

11.

Kagaya, Yuki, et al.. (2022). ContactPFP: Protein function prediction using predicted contact information. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

12.

Kagaya, Yuki, et al.. (2022). ContactPFP: Protein Function Prediction Using Predicted Contact Information. SHILAP Revista de lepidopterología. 2. 6 indexed citations

13.

Jain, Aashish, et al.. (2021). Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction. Scientific Reports. 11(1). 7574–7574. 21 indexed citations

14.

Terashi, Genki, et al.. (2021). Protein contact map refinement for improving structure prediction using generative adversarial networks. Bioinformatics. 37(19). 3168–3174. 10 indexed citations

15.

Kagaya, Yuki, et al.. (2020). A novel circular ssDNA virus of the phylum Cressdnaviricota discovered in metagenomic data from otter clams (Lutraria rhynchaena). Archives of Virology. 165(12). 2921–2926. 1 indexed citations

16.

Kagaya, Yuki, Lua T. Dang, Hoa Thi Nguyen, et al.. (2020). Metagenome Sequences from the Environment of Diseased Otter Clams, Lutraria rhynchaena, from a Farm in Vietnam. Microbiology Resource Announcements. 9(2). 2 indexed citations

17.

Terashi, Genki, Yuki Kagaya, & Daisuke Kihara. (2020). MAINMASTseg: Automated Map Segmentation Method for Cryo-EM Density Maps with Symmetry. Journal of Chemical Information and Modeling. 60(5). 2634–2643. 7 indexed citations

18.

Obayashi, Takeshi, Yuki Kagaya, Yuichi Aoki, Shu Tadaka, & Kengo Kinoshita. (2018). COXPRESdb v7: a gene coexpression database for 11 animal species supported by 23 coexpression platforms for technical evaluation and evolutionary inference. Nucleic Acids Research. 47(D1). D55–D62. 98 indexed citations

19.

Obayashi, Takeshi, Yuichi Aoki, Shu Tadaka, Yuki Kagaya, & Kengo Kinoshita. (2017). ATTED-II in 2018: A Plant Coexpression Database Based on Investigation of the Statistical Property of the Mutual Rank Index. Plant and Cell Physiology. 59(1). e3–e3. 190 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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