Nozomi Nagano

2.3k total citations
35 papers, 1.7k citations indexed

About

Nozomi Nagano is a scholar working on Molecular Biology, Materials Chemistry and Pharmacology. According to data from OpenAlex, Nozomi Nagano has authored 35 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 11 papers in Materials Chemistry and 7 papers in Pharmacology. Recurrent topics in Nozomi Nagano's work include Protein Structure and Dynamics (13 papers), Enzyme Structure and Function (11 papers) and Microbial Natural Products and Biosynthesis (7 papers). Nozomi Nagano is often cited by papers focused on Protein Structure and Dynamics (13 papers), Enzyme Structure and Function (11 papers) and Microbial Natural Products and Biosynthesis (7 papers). Nozomi Nagano collaborates with scholars based in Japan, United Kingdom and United States. Nozomi Nagano's co-authors include Janet M. Thornton, Christine Orengo, Myco Umemura, Motonori Ota, Yöichi Iitaka, Katsuyuki Aoki, Masayuki Machida, Kazuo Shin‐ya, Tomoko Ishii and Kiyoshi Asai and has published in prestigious journals such as Nature, Nucleic Acids Research and Angewandte Chemie International Edition.

In The Last Decade

Nozomi Nagano

32 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nozomi Nagano Japan 17 1.3k 441 380 180 139 35 1.7k
Julian C.‐H. Chen United States 18 922 0.7× 278 0.6× 210 0.6× 186 1.0× 82 0.6× 27 1.4k
Brendan M. Duggan United States 23 1.2k 1.0× 163 0.4× 589 1.6× 165 0.9× 239 1.7× 50 1.9k
Subramanyam Swaminathan United States 27 1.4k 1.1× 385 0.9× 91 0.2× 135 0.8× 91 0.7× 80 2.6k
Mario Bouchard United Kingdom 12 1.6k 1.3× 273 0.6× 313 0.8× 38 0.2× 124 0.9× 16 2.1k
Shoshana Brown United States 20 1.4k 1.1× 371 0.8× 154 0.4× 81 0.5× 69 0.5× 37 1.8k
Joel M. Harp United States 27 1.7k 1.3× 283 0.6× 141 0.4× 129 0.7× 33 0.2× 72 2.4k
Klaus Reuter Germany 22 1.3k 1.0× 365 0.8× 175 0.5× 46 0.3× 38 0.3× 63 1.5k
G. Jogl United States 28 1.6k 1.3× 343 0.8× 104 0.3× 96 0.5× 35 0.3× 55 2.2k
Daniel O. Cicero Italy 25 1.3k 1.0× 312 0.7× 88 0.2× 182 1.0× 55 0.4× 122 2.3k
M.E. Fraser Canada 24 1.3k 1.0× 587 1.3× 52 0.1× 88 0.5× 195 1.4× 60 2.1k

Countries citing papers authored by Nozomi Nagano

Since Specialization
Citations

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

Fields of papers citing papers by Nozomi Nagano

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nozomi Nagano

This figure shows the co-authorship network connecting the top 25 collaborators of Nozomi Nagano. A scholar is included among the top collaborators of Nozomi Nagano 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 Nozomi Nagano. Nozomi Nagano 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
1.
Nagano, Nozomi, Masami Ikeda, Makoto Miwa, et al.. (2023). A novel corpus of molecular to higher-order events that facilitates the understanding of the pathogenic mechanisms of idiopathic pulmonary fibrosis. Scientific Reports. 13(1). 5986–5986.
2.
3.
Ye, Ying, Atsushi Minami, Miho Izumikawa, et al.. (2016). Unveiling the Biosynthetic Pathway of the Ribosomally Synthesized and Post‐translationally Modified Peptide Ustiloxin B in Filamentous Fungi. Angewandte Chemie International Edition. 55(28). 8072–8075. 97 indexed citations
4.
Nagano, Nozomi, et al.. (2014). EzCatDB: the enzyme reaction database, 2015 update. Nucleic Acids Research. 43(D1). D453–D458. 25 indexed citations
5.
Nagao, Chioko, Nozomi Nagano, & Kenji Mizuguchi. (2014). Prediction of Detailed Enzyme Functions and Identification of Specificity Determining Residues by Random Forests. PLoS ONE. 9(1). e84623–e84623. 30 indexed citations
6.
Umemura, Myco, Hideaki Koike, Nozomi Nagano, et al.. (2013). MIDDAS-M: Motif-Independent De Novo Detection of Secondary Metabolite Gene Clusters through the Integration of Genome Sequencing and Transcriptome Data. PLoS ONE. 8(12). e84028–e84028. 85 indexed citations
7.
Kato, Tsuyoshi & Nozomi Nagano. (2011). Discriminative structural approaches for enzyme active-site prediction. BMC Bioinformatics. 12(S1). S49–S49. 3 indexed citations
8.
Koike, Ryotaro, Kana Shimizu, Matsuyuki Shirota, et al.. (2010). SAHG, a comprehensive database of predicted structures of all human proteins. Nucleic Acids Research. 39(suppl_1). D487–D493. 9 indexed citations
9.
Kato, Tsuyoshi, et al.. (2010). Parametric Templates: A New Enzyme Active-Site Prediction Algorithm. 711–718. 1 indexed citations
10.
Kato, Tsuyoshi & Nozomi Nagano. (2010). Metric learning for enzyme active-site search. Bioinformatics. 26(21). 2698–2704. 16 indexed citations
11.
Nagao, Chioko, Nozomi Nagano, & Kenji Mizuguchi. (2010). Relationships between functional subclasses and information contained in active‐site and ligand‐binding residues in diverse superfamilies. Proteins Structure Function and Bioinformatics. 78(10). 2369–2384. 6 indexed citations
12.
Nagano, Nozomi. (2004). EzCatDB: the Enzyme Catalytic-mechanism Database. Nucleic Acids Research. 33(Database issue). D407–D412. 54 indexed citations
13.
Asai, Kiyoshi, et al.. (2003). Systematic Analyses of P-Loop Containing Nucleotide Triphosphate Hydrolase Superfamily Based on Sequence, Structure and Function. Proceedings Genome Informatics Workshop/Genome informatics. 14. 581–582. 3 indexed citations
14.
Gilbert, David, David R. Westhead, Nozomi Nagano, & Janet M. Thornton. (1999). Motif-based searching in TOPS protein topology databases.. Bioinformatics. 15(4). 317–326. 68 indexed citations
15.
Nagano, Nozomi, et al.. (1999). Strong hydrophobic nature of cysteine residues in proteins. FEBS Letters. 458(1). 69–71. 128 indexed citations
16.
Nagano, Nozomi, E. Gail Hutchinson, & Janet M. Thornton. (1999). Barrel structures in proteins: Automatic identification and classification including a sequence analysis of TIM barrels. Protein Science. 8(10). 2072–2084. 56 indexed citations
17.
Nagano, Nozomi & Nozomi Nagano. (1997). Mechanism of Decoding mRNA in Protein Biosynthesis. YAKUGAKU ZASSHI. 117(10-11). 749–763.
18.
Saito, Kazuki, Masahiro Imoto, Nozomi Nagano, Masaru Toriyama, & Toshio Nakajima. (1995). Such Hydrophobic Peptides as Dansylated Mastoparan Can Elevate the Fertilization Membrane of Sea Urchin Eggs. Biochemical and Biophysical Research Communications. 215(3). 828–834. 4 indexed citations
19.
Nagano, Nozomi, H. Takagi, & Michal Harel. (1991). The side-by-side model of two tRNA molecules allowing the α-helical conformation of the nascent polypeptide during the ribosomal transpeptidation. Biochimie. 73(7-8). 947–960. 10 indexed citations
20.
Nagano, Nozomi. (1984). Prediction of packing of secondary structure. Advances in Biophysics. 18. 115–148. 4 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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