Tamako Matsuhashi

456 total citations
17 papers, 333 citations indexed

About

Tamako Matsuhashi is a scholar working on Genetics, Molecular Biology and Cancer Research. According to data from OpenAlex, Tamako Matsuhashi has authored 17 papers receiving a total of 333 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Genetics, 8 papers in Molecular Biology and 7 papers in Cancer Research. Recurrent topics in Tamako Matsuhashi's work include Genetic and phenotypic traits in livestock (8 papers), Cancer-related molecular mechanisms research (7 papers) and Genetic Mapping and Diversity in Plants and Animals (4 papers). Tamako Matsuhashi is often cited by papers focused on Genetic and phenotypic traits in livestock (8 papers), Cancer-related molecular mechanisms research (7 papers) and Genetic Mapping and Diversity in Plants and Animals (4 papers). Tamako Matsuhashi collaborates with scholars based in Japan and United States. Tamako Matsuhashi's co-authors include Naohiko Kobayashi, Shin‐nosuke Takeshima, Yoko Aida, Ryuichi Masuda, Tetsuo Nunoya, Koichi Murata, Hideyuki Mannen, Shinji Sasazaki, Junko Kohara and Takashi Ohmori and has published in prestigious journals such as PLoS ONE, Gene and BMC Genetics.

In The Last Decade

Tamako Matsuhashi

17 papers receiving 320 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Tamako Matsuhashi Japan 10 185 135 115 73 60 17 333
A. Ruść Poland 14 298 1.6× 125 0.9× 36 0.3× 20 0.3× 68 1.1× 30 456
Andrej Razpet Slovenia 9 217 1.2× 100 0.7× 33 0.3× 6 0.1× 55 0.9× 13 344
Charu G. Kumar United States 8 223 1.2× 33 0.2× 36 0.3× 105 1.4× 26 0.4× 8 391
Sahar Ahmed Egypt 8 52 0.3× 55 0.4× 28 0.2× 34 0.5× 16 0.3× 33 225
Kenneth Escudero United States 6 127 0.7× 122 0.9× 152 1.3× 116 1.6× 22 0.4× 6 413
Kimberly M Davenport United States 12 201 1.1× 75 0.6× 40 0.3× 16 0.2× 60 1.0× 35 350
Ramesh Kumar Vijh India 10 211 1.1× 76 0.6× 15 0.1× 16 0.2× 85 1.4× 41 335
Robert Mukiibi United Kingdom 12 234 1.3× 89 0.7× 14 0.1× 11 0.2× 101 1.7× 26 346
Daniel E. Goszczynski Argentina 12 284 1.5× 63 0.5× 15 0.1× 14 0.2× 54 0.9× 26 414
Julien Sarry France 13 485 2.6× 173 1.3× 44 0.4× 8 0.1× 130 2.2× 18 702

Countries citing papers authored by Tamako Matsuhashi

Since Specialization
Citations

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

Fields of papers citing papers by Tamako Matsuhashi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Tamako Matsuhashi

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

All Works

17 of 17 papers shown
1.
Oyama, Kenji, et al.. (2020). Effect of DNA markers on the fertility traits of Japanese Black cattle for improving beef quantity and quality. Archives animal breeding/Archiv für Tierzucht. 63(1). 9–17. 9 indexed citations
2.
Hirano, Takashi, Tamako Matsuhashi, Kenji Takeda, et al.. (2016). IARS mutation causes prenatal death in Japanese Black cattle. Animal Science Journal. 87(9). 1178–1181. 7 indexed citations
3.
Ibi, Takayuki, Naohiko Kobayashi, Tamako Matsuhashi, et al.. (2015). Allelic frequencies and association with carcass traits of six genes in local subpopulations of Japanese Black cattle. Animal Science Journal. 87(4). 469–476. 9 indexed citations
4.
Sasaki, Shinji, Takayuki Ibi, Tamako Matsuhashi, et al.. (2015). Genetic variants in the upstream region of activin receptor IIA are associated with female fertility in Japanese Black cattle. BMC Genetics. 16(1). 123–123. 10 indexed citations
5.
Ishii, Atsushi, Yoshinobu Uemoto, Eiji Kobayashi, et al.. (2015). The g.841G>C SNP of FASN gene is associated with fatty acid composition in beef cattle. Animal Science Journal. 86(8). 737–746. 28 indexed citations
6.
Matsuhashi, Tamako. (2014). Physical and physiological characteristics of IARS disorder. 42(2). 71–77. 1 indexed citations
7.
Kobayashi, Eiji, et al.. (2013). Gene Flow of Domestic Type to Japanese Wild Boars in Growth Hormone Gene. Nihon Yoton Gakkaishi. 50(3). 137–141. 3 indexed citations
8.
Sasaki, Shinji, Takayuki Ibi, Toshio Watanabe, et al.. (2013). Variants in the 3' UTR of General Transcription Factor IIF, polypeptide 2 affect female calving efficiency in Japanese Black cattle. BMC Genetics. 14(1). 41–41. 15 indexed citations
9.
Hirano, Takashi, Naohiko Kobayashi, Tamako Matsuhashi, et al.. (2013). Mapping and Exome Sequencing Identifies a Mutation in the IARS Gene as the Cause of Hereditary Perinatal Weak Calf Syndrome. PLoS ONE. 8(5). e64036–e64036. 26 indexed citations
10.
Ishii, Atsushi, Yoshinobu Uemoto, Eiji Kobayashi, et al.. (2013). Genome‐wide association study for fatty acid composition in Japanese Black cattle. Animal Science Journal. 84(10). 675–682. 41 indexed citations
11.
Ibi, Takayuki, Yoshihiro Tanabe, Naohiko Kobayashi, et al.. (2013). The assessment of genetic diversity within and among the eight subpopulations of Japanese Black cattle using 52 microsatellite markers. Animal Science Journal. 84(8). 585–591. 13 indexed citations
12.
Takeshima, Shin‐nosuke, Hironobu Murakami, Junko Kohara, et al.. (2012). BLV-CoCoMo-qPCR: a useful tool for evaluating bovine leukemia virus infection status. BMC Veterinary Research. 8(1). 167–167. 71 indexed citations
13.
Takeshima, Shin‐nosuke, Yuki Matsumoto, Naohiko Kobayashi, et al.. (2011). BLV-CoCoMo-qPCR: comparison of other detection methods for BLV infection and kinetics analysis in experimental transmission of BLV in cattle. Retrovirology. 8(S1). 2 indexed citations
14.
Miyasaka, T., Shin‐nosuke Takeshima, Yuki Matsumoto, et al.. (2010). The diversity of bovine MHC class II DRB3 and DQA1 alleles in different herds of Japanese Black and Holstein cattle in Japan. Gene. 472(1-2). 42–49. 56 indexed citations
15.
Nagai, Kouhei, Hideya Kawaji, Naohiko Kobayashi, et al.. (2008). Developing an integrated database system for the large-scale proteomic analysis of Japanese Black cattle. Nihon Chikusan Gakkaiho. 79(4). 467–481. 1 indexed citations
16.
Mano, Tsutomu, Hifumi Tsuruga, Tamako Matsuhashi, et al.. (2003). Polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method for mtDNA typing in hokkaido brown bear (Ursus arctos yesoensis).. PubMed. 50(4). 195–9. 1 indexed citations
17.
Matsuhashi, Tamako, et al.. (2001). Phylogenetic Relationships among Worldwide Populations of the Brown Bear Ursus arctos. ZOOLOGICAL SCIENCE. 18(8). 1137–1143. 40 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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