Shiori Yabe

621 total citations
33 papers, 402 citations indexed

About

Shiori Yabe is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Shiori Yabe has authored 33 papers receiving a total of 402 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Plant Science, 22 papers in Genetics and 4 papers in Molecular Biology. Recurrent topics in Shiori Yabe's work include Genetic Mapping and Diversity in Plants and Animals (20 papers), Genetics and Plant Breeding (14 papers) and Genetic and phenotypic traits in livestock (10 papers). Shiori Yabe is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (20 papers), Genetics and Plant Breeding (14 papers) and Genetic and phenotypic traits in livestock (10 papers). Shiori Yabe collaborates with scholars based in Japan, United States and Brazil. Shiori Yabe's co-authors include Hiroyoshi Iwata, Hiroshi Nakagawa, Ryo Ohsawa, Hiroe Yoshida, Yukiko Senga, Maiko Kagami, Masanori Yamasaki, Jean‐Luc Jannink, Masaki Okamura and Shunsuke Adachi and has published in prestigious journals such as PLoS ONE, Water Research and Scientific Reports.

In The Last Decade

Shiori Yabe

28 papers receiving 395 citations

Peers

Shiori Yabe

GENET

ECOLO

Herry S. Utomo United States

Bunmi Olasanmi Nigeria

M. Rahmanian Syria

Hassan M. El Shaer Egypt

Yasir Serag Alnor Gorafi Sudan

Travis L. Goron Canada

E. Bettencourt Portugal

Kaushik Das India

Gabriele Orasen Italy

Ved Prakash India

Herry S. Utomo United States

Shiori Yabe

208 ×0.7

60 ×0.3

GENET

14 ×0.2

41 ×1.0

ECOLO

74 ×2.0

Citations per year, relative to Shiori Yabe Shiori Yabe (= 1×) peers Herry S. Utomo

Countries citing papers authored by Shiori Yabe

Since Specialization

Citations

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

Fields of papers citing papers by Shiori Yabe

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shiori Yabe

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Shirasawa, Kenta, Hiroshi Yakushiji, Takeshi Hayashi, et al.. (2024). Genome-wide association studies using chromosome-scale genomes of male and female lines redefines two sex-linked loci in linkage disequilibrium in Ficus carica L.. Scientia Horticulturae. 336. 113424–113424. 1 indexed citations

Kitazawa, Noriyuki, Ayahiko Shomura, Tatsumi Mizubayashi, et al.. (2024). Development of SNP genotyping assays for heading date in rice. Breeding Science. 74(3). 274–284. 2 indexed citations

Tanaka, R., Taiji Kawakatsu, Nobuhiro Tanaka, et al.. (2024). Transcriptome-based prediction for polygenic traits in rice using different gene subsets. BMC Genomics. 25(1). 915–915.

Yamamoto, Eiji, Shiori Yabe, Hideki Yoshida, et al.. (2023). Independent control of organ number and distribution pattern in rice panicle. Frontiers in Plant Science. 14. 1119770–1119770. 2 indexed citations

Yamasaki, Masanori, et al.. (2023). Dual ensemble approach to predict rice heading date by integrating multiple rice phenology models and machine learning-based genetic parameter regression models. Agricultural and Forest Meteorology. 344. 109821–109821. 9 indexed citations

Wei, Shu‐Jun, R. Tanaka, Taiji Kawakatsu, et al.. (2023). Genome- and Transcriptome-wide Association Studies to Discover Candidate Genes for Diverse Root Phenotypes in Cultivated Rice. Rice. 16(1). 55–55. 6 indexed citations

Takeshima, Ryoma, Shiori Yabe, & Katsuhiro Matsui. (2022). Genetic basis of maturity time is independent from that of flowering time and contributes to ecotype differentiation in common buckwheat (Fagopyrum esculentum Moench). BMC Plant Biology. 22(1). 353–353. 4 indexed citations

Yabe, Shiori, Hiroe Yoshida, Masanori Yamasaki, et al.. (2022). A novel index to evaluate resource allocation pattern in panicles in Japanese rice cultivars. Plant Production Science. 25(2). 195–210.

Shirasawa, Kenta, Hiroshi Yakushiji, Shiori Yabe, et al.. (2021). Analysis of the Segregation Distortion of FcRAN1 Genotypes Based on Whole-Genome Resequencing of Fig (Ficus carica L.) Breeding Parents. Frontiers in Plant Science. 12. 647599–647599. 2 indexed citations

10.

Sekine, Daisuke, Mai Tsuda, Shiori Yabe, et al.. (2021). Improving Quantitative Traits in Self-Pollinated Crops Using Simulation-Based Selection With Minimal Crossing. Frontiers in Plant Science. 12. 729645–729645. 3 indexed citations

11.

Arai‐Sanoh, Yumiko, Masaki Okamura, Shiori Yabe, et al.. (2021). Estimation Accuracy of Growth Traits in Rice using Nondestructive Sensing. Japanese Journal of Crop Science. 90(2). 160–167.

12.

Yabe, Shiori & Hiroyoshi Iwata. (2020). Genomics-assisted breeding in minor and pseudo-cereals. Breeding Science. 70(1). 19–31. 17 indexed citations

13.

Jarquín, Diego, Hiromi Kajiya‐Kanegae, Shiori Yabe, et al.. (2020). Coupling day length data and genomic prediction tools for predicting time-related traits under complex scenarios. Scientific Reports. 10(1). 13382–13382. 10 indexed citations

14.

Senga, Yukiko, et al.. (2018). Influence of parasitic chytrids on the quantity and quality of algal dissolved organic matter (AOM). Water Research. 145. 346–353. 46 indexed citations

15.

Yabe, Shiori, Takashi Hara, Mariko Ueno, et al.. (2018). Potential of Genomic Selection in Mass Selection Breeding of an Allogamous Crop: An Empirical Study to Increase Yield of Common Buckwheat. Frontiers in Plant Science. 9. 276–276. 35 indexed citations

16.

Yabe, Shiori, Hiroe Yoshida, Hiromi Kajiya‐Kanegae, et al.. (2018). Description of grain weight distribution leading to genomic selection for grain-filling characteristics in rice. PLoS ONE. 13(11). e0207627–e0207627. 28 indexed citations

17.

Yabe, Shiori, Hiroyoshi Iwata, & Jean‐Luc Jannink. (2017). A Simple Package to Script and Simulate Breeding Schemes: The Breeding Scheme Language. Crop Science. 57(3). 1347–1354. 21 indexed citations

18.

Yabe, Shiori, Masanori Yamasaki, Kaworu Ebana, Takeshi Hayashi, & Hiroyoshi Iwata. (2016). Island-Model Genomic Selection for Long-Term Genetic Improvement of Autogamous Crops. PLoS ONE. 11(4). e0153945–e0153945. 16 indexed citations

19.

Yabe, Shiori, Takashi Hara, Mariko Ueno, et al.. (2014). Rapid genotyping with DNA micro-arrays for high-density linkage mapping and QTL mapping in common buckwheat (Fagopyrum esculentum Moench). Breeding Science. 64(4). 291–299. 29 indexed citations

20.

Yabe, Shiori, Élcio Leal, Hirohisa Kishino, et al.. (2013). Codon pairs of the HIV-1 vif gene correlate with CD4+ T cell count. BMC Infectious Diseases. 13(1). 173–173. 7 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.

Explore authors with similar magnitude of impact