S. Hosoya

2.6k total citations · 1 hit paper
74 papers, 1.9k citations indexed

About

S. Hosoya is a scholar working on Genetics, Radiation and Ecology. According to data from OpenAlex, S. Hosoya has authored 74 papers receiving a total of 1.9k indexed citations (citations by other indexed papers that have themselves been cited), including 24 papers in Genetics, 18 papers in Radiation and 13 papers in Ecology. Recurrent topics in S. Hosoya's work include Genetic diversity and population structure (12 papers), X-ray Spectroscopy and Fluorescence Analysis (11 papers) and Aquaculture disease management and microbiota (10 papers). S. Hosoya is often cited by papers focused on Genetic diversity and population structure (12 papers), X-ray Spectroscopy and Fluorescence Analysis (11 papers) and Aquaculture disease management and microbiota (10 papers). S. Hosoya collaborates with scholars based in Japan, Canada and Singapore. S. Hosoya's co-authors include Kiyoshi Kikuchi, Luis O.B. Afonso, Stewart C. Johnson, T. Fukamachi, Mark D. Fast, Yuzuru Suzuki, Hiroaki Suetake, Wataru Kai, Satoshi Tasumi and Naoki Mizuno and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and The Journal of Immunology.

In The Last Decade

S. Hosoya

71 papers receiving 1.9k citations

Hit Papers

A Trans-Species Missense SNP in Amhr2 Is Associated with ... 2012 2026 2016 2021 2012 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
S. Hosoya Japan 21 804 440 388 369 311 74 1.9k
George B. Chapman United States 29 245 0.3× 117 0.3× 75 0.2× 256 0.7× 791 2.5× 113 2.3k
Juan A. Subirana Spain 36 616 0.8× 70 0.2× 118 0.3× 46 0.1× 3.2k 10.1× 213 4.7k
Jürgen Markl Germany 39 300 0.4× 257 0.6× 25 0.1× 1.7k 4.7× 1.3k 4.1× 117 3.9k
Helmut Wieczorek Germany 43 318 0.4× 89 0.2× 142 0.4× 423 1.1× 4.0k 12.8× 107 5.6k
Sakae Tsuda Japan 35 231 0.3× 167 0.4× 31 0.1× 137 0.4× 1.4k 4.4× 117 3.3k
Chi-Hing C. Cheng United States 23 384 0.5× 295 0.7× 56 0.1× 99 0.3× 603 1.9× 33 2.2k
Robert Zwilling Germany 23 209 0.3× 204 0.5× 30 0.1× 216 0.6× 887 2.9× 56 1.8k
Motonori Hoshi Japan 31 268 0.3× 409 0.9× 605 1.6× 216 0.6× 1.3k 4.1× 144 3.0k
L.Dennis Smith United States 39 786 1.0× 121 0.3× 529 1.4× 99 0.3× 2.3k 7.5× 86 4.3k
Norio Suzuki Japan 24 210 0.3× 302 0.7× 335 0.9× 95 0.3× 473 1.5× 70 1.5k

Countries citing papers authored by S. Hosoya

Since Specialization
Citations

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

Fields of papers citing papers by S. Hosoya

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of S. Hosoya

This figure shows the co-authorship network connecting the top 25 collaborators of S. Hosoya. A scholar is included among the top collaborators of S. Hosoya 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 S. Hosoya. S. Hosoya 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
2.
Kikuchi, Kiyoshi, et al.. (2024). Automated phenotyping empowered by deep learning for genomic prediction of body size in the tiger pufferfish, Takifugu rubripes. Aquaculture. 595. 741491–741491. 4 indexed citations
3.
Taslima, Khanam, Kiyoshi Kikuchi, & S. Hosoya. (2024). Genomic architecture and sex chromosome systems of commercially important fish species in Asia – Current status, knowledge gaps and future prospects. Reviews in Aquaculture. 16(4). 1918–1946. 4 indexed citations
5.
Sato, Yoshiki, Satoshi Tasumi, Naoki Mizuno, et al.. (2023). l-fucoside localization in the gills of the genus Takifugu and its possible implication in the parasitism of Heterobothrium okamotoi (Monogenea: Diclidophoridae). Biochimica et Biophysica Acta (BBA) - General Subjects. 1867(12). 130467–130467.
6.
Hosoya, S., Takashi Koyama, Shotaro Hirase, et al.. (2022). Repeated translocation of a supergene underlying rapid sex chromosome turnover in Takifugu pufferfish. Proceedings of the National Academy of Sciences. 119(23). e2121469119–e2121469119. 30 indexed citations
8.
Hosoya, S., et al.. (2021). Genomic prediction for testes weight of the tiger pufferfish, Takifugu rubripes, using medium to low density SNPs. Scientific Reports. 11(1). 20372–20372. 13 indexed citations
9.
Koyama, Takashi, Masatoshi Nakamoto, Kagayaki Morishima, et al.. (2019). A SNP in a Steroidogenic Enzyme Is Associated with Phenotypic Sex in Seriola Fishes. Current Biology. 29(11). 1901–1909.e8. 84 indexed citations
10.
Hosoya, S., Shotaro Hirase, Kiyoshi Kikuchi, et al.. (2019). Random PCR‐based genotyping by sequencing technology GRAS‐Di (genotyping by random amplicon sequencing, direct) reveals genetic structure of mangrove fishes. Molecular Ecology Resources. 19(5). 1153–1163. 55 indexed citations
11.
Hosoya, S., Takashi Kamiya, Satoshi Tasumi, et al.. (2018). Identification of the sex-determining locus in grass puffer (Takifugu niphobles) provides evidence for sex-chromosome turnover in a subset of Takifugu species. PLoS ONE. 13(1). e0190635–e0190635. 41 indexed citations
12.
Hosoya, S., Kiyoshi Kikuchi, Hiroshi Nagashima, et al.. (2018). Assessment of genetic diversity in Coho salmon (Oncorhynchus kisutch) populations with no family records using ddRAD-seq. BMC Research Notes. 11(1). 548–548. 20 indexed citations
13.
Tan, Engkong, Naoki Mizuno, S. Hosoya, et al.. (2018). Transcriptomic analysis of immunoglobulin novel antigen receptor (IgNAR) heavy chain constant domains of brownbanded bamboo shark (Chiloscyllium punctatum). Fish & Shellfish Immunology. 84. 370–376. 12 indexed citations
14.
Kikuchi, Kiyoshi, S. Hosoya, & Satoshi Tasumi. (2013). Sex determination in fish. 41(1). 37–48. 3 indexed citations
15.
Hosoya, S., Wataru Kai, Masashi Fujita, et al.. (2012). THE GENETIC ARCHITECTURE OF GROWTH RATE IN JUVENILETAKIFUGUSPECIES. Evolution. 67(2). 590–598. 27 indexed citations
16.
Hosoya, S., Stewart C. Johnson, George K. Iwama, A. Kurt Gamperl, & Luis O.B. Afonso. (2006). Changes in free and total plasma cortisol levels in juvenile haddock (Melanogrammus aeglefinus) exposed to long-term handling stress. Comparative Biochemistry and Physiology Part A Molecular & Integrative Physiology. 146(1). 78–86. 54 indexed citations
17.
Hosoya, S., et al.. (1979). Mechanism of rapid delignification during alkaline cooking with addition of tetrahydroanthraquinone. THE journal. 25(3). 239–240. 4 indexed citations
18.
Nakajima, Tetsuo, T. Fukamachi, Osamu Terasaki, & S. Hosoya. (1976). The detection of small differences in lattice constant at low temperature by an energy-dispersive X-ray diffractometer. Journal of Applied Crystallography. 9(4). 286–290. 7 indexed citations
19.
Ando, Masahiko, S. Hosoya, & K. Namikawa. (1976). Characteristics of a channel plate as an image intensifier for X-ray topography. Journal of Applied Crystallography. 9(4). 269–272. 16 indexed citations
20.
Fukamachi, T. & S. Hosoya. (1972). Binding effect due to K electrons observed on a Compton profile of Si. Physics Letters A. 41(5). 416–418. 5 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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