Genki Terashi

2.2k total citations
62 papers, 819 citations indexed

About

Genki Terashi is a scholar working on Molecular Biology, Materials Chemistry and Structural Biology. According to data from OpenAlex, Genki Terashi has authored 62 papers receiving a total of 819 indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Molecular Biology, 33 papers in Materials Chemistry and 28 papers in Structural Biology. Recurrent topics in Genki Terashi's work include Protein Structure and Dynamics (32 papers), Enzyme Structure and Function (30 papers) and Advanced Electron Microscopy Techniques and Applications (28 papers). Genki Terashi is often cited by papers focused on Protein Structure and Dynamics (32 papers), Enzyme Structure and Function (30 papers) and Advanced Electron Microscopy Techniques and Applications (28 papers). Genki Terashi collaborates with scholars based in United States, Japan and France. Genki Terashi's co-authors include Daisuke Kihara, Xiao Wang, Charles Christoffer, Mayuko Takeda‐Shitaka, Hideaki Umeyama, Amitava Roy, Kazuhiko Kanou, Tunde Aderinwale, Yuki Kagaya and Daisuke Takaya and has published in prestigious journals such as Nature, Nature Communications and Nano Letters.

In The Last Decade

Genki Terashi

59 papers receiving 811 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Genki Terashi United States	17	638	366	263	110	86	62	819
Martin Klumpp Switzerland	15	581 0.9×	255 0.7×	33 0.1×	58 0.5×	18 0.2×	19	670
Elina Tjioe United States	11	669 1.0×	264 0.7×	72 0.3×	30 0.3×	10 0.1×	13	806
Ivan Teo United States	7	265 0.4×	94 0.3×	90 0.3×	35 0.3×	19 0.2×	8	336
Charles Christoffer United States	14	469 0.7×	182 0.5×	26 0.1×	147 1.3×	6 0.1×	25	543
Shruthi Viswanath United States	12	412 0.6×	100 0.3×	46 0.2×	30 0.3×	7 0.1×	24	455
Tristan Cragnolini United Kingdom	12	539 0.8×	120 0.3×	28 0.1×	47 0.4×	9 0.1×	21	631
Ludwig Sinn Germany	12	546 0.9×	53 0.1×	64 0.2×	20 0.2×	22 0.3×	19	721
Helen M. E. Duyvesteyn United Kingdom	10	167 0.3×	127 0.3×	53 0.2×	19 0.2×	12 0.1×	19	301
Petrus H. Zwart United States	12	353 0.6×	216 0.6×	48 0.2×	9 0.1×	8 0.1×	29	620
Thomas J. Oldfield United Kingdom	10	595 0.9×	246 0.7×	15 0.1×	102 0.9×	8 0.1×	18	692

Countries citing papers authored by Genki Terashi

Since Specialization

Citations

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

Fields of papers citing papers by Genki Terashi

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Genki Terashi

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

All Works

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20 of 20 papers shown

Terashi, Genki, Roland Riek, Jason Greenwald, et al.. (2025). Structural Insights and Functional Dynamics of β-Lactoglobulin Fibrils. Nano Letters. 25(45). 16146–16153.

Mukherjee, Somnath, Martin Gustavsson, James R. Fuller, et al.. (2025). Effect of phosphorylation barcodes on arrestin binding to a chemokine receptor. Nature. 643(8070). 280–287. 3 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.

Terashi, Genki, et al.. (2025). Advancing structure modeling from cryo-EM maps with deep learning. Biochemical Society Transactions. 53(1). 259–265. 2 indexed citations

Terashi, Genki, et al.. (2025). DMcloud: Macromolecular Structure Modeling with Local Structure Fitting for Medium to Low Resolution Cryo-EM Maps. Microscopy and Microanalysis. 31(Supplement_1).

Wang, Xiao, et al.. (2024). DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model. Nature Methods. 21(12). 2307–2317. 18 indexed citations

Bou‐Abdallah, Fadi, et al.. (2024). Unveiling the stochastic nature of human heteropolymer ferritin self‐assembly mechanism. Protein Science. 33(8). e5104–e5104. 9 indexed citations

Terashi, Genki, et al.. (2023). Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling. Bioinformatics. 39(8). 5–7. 7 indexed citations

Wang, Xiao, Genki Terashi, & Daisuke Kihara. (2023). CryoREAD: De novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

10.

Wang, Xiao, Genki Terashi, & Daisuke Kihara. (2023). CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nature Methods. 20(11). 1739–1747. 22 indexed citations

11.

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

12.

Terashi, Genki, Xiao Wang, & Daisuke Kihara. (2022). Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score. Acta Crystallographica Section D Structural Biology. 79(1). 10–21. 9 indexed citations

13.

Terashi, Genki, et al.. (2022). Residue-wise local quality estimation for protein models from cryo-EM maps. Nature Methods. 19(9). 1116–1125. 26 indexed citations

14.

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

15.

Ramadesikan, Swetha, Jennifer Lee, Kayalvizhi Madhivanan, et al.. (2021). Genotype & phenotype in Lowe Syndrome: specific OCRL1 patient mutations differentially impact cellular phenotypes. Human Molecular Genetics. 30(3-4). 198–212. 13 indexed citations

16.

Wang, Xiao, et al.. (2021). Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning. Nature Communications. 12(1). 2302–2302. 29 indexed citations

17.

Mori, Takaharu, Genki Terashi, Daisuke Matsuoka, Daisuke Kihara, & Yuji Sugita. (2021). Efficient Flexible Fitting Refinement with Automatic Error Fixing for De Novo Structure Modeling from Cryo-EM Density Maps. Journal of Chemical Information and Modeling. 61(7). 3516–3528. 11 indexed citations

18.

Terashi, Genki, et al.. (2019). Protein secondary structure detection in intermediate-resolution cryo-EM maps using deep learning. Nature Methods. 16(9). 911–917. 60 indexed citations

19.

Terashi, Genki & Daisuke Kihara. (2018). De novo main-chain modeling for EM maps using MAINMAST. Nature Communications. 9(1). 1618–1618. 78 indexed citations

20.

Terashi, Genki & Daisuke Kihara. (2018). De novo main-chain modeling with MAINMAST in 2015/2016 EM Model Challenge. Journal of Structural Biology. 204(2). 351–359. 13 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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