Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Expression of Xa1 , a bacterial blight-resistance gene in rice, is induced by bacterial inoculation
1998512 citationsSatomi Yoshimura, Utako Yamanouchi et al.Proceedings of the National Academy of Sciencesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Nobuo Iwata'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 Nobuo Iwata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nobuo Iwata more than expected).
This network shows the impact of papers produced by Nobuo Iwata. 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 Nobuo Iwata. The network helps show where Nobuo Iwata may publish in the future.
Co-authorship network of co-authors of Nobuo Iwata
This figure shows the co-authorship network connecting the top 25 collaborators of Nobuo Iwata.
A scholar is included among the top collaborators of Nobuo Iwata 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 Nobuo Iwata. Nobuo Iwata is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sun, Chuan Qing, et al.. (1998). RFLP analysis on mitochondrial DNA in common wild rice (O.rufipogon Griff.) and cultivated rice (O.sativa L.). Acta Genetica Sinica. 25(1). 43–45.3 indexed citations
3.
Yasui, Hideshi, et al.. (1998). RFLP Mapping of Genes for Resistance to Green Rice Leafhopper (Nephotettix Cincticeps Uhler) in Rice Cultivar DV85 Using Near Isogenic Lines. 52(3). 169–175.13 indexed citations
4.
Yoshimura, Satomi, Utako Yamanouchi, Yūichi Katayose, et al.. (1998). Expression of Xa1 , a bacterial blight-resistance gene in rice, is induced by bacterial inoculation. Proceedings of the National Academy of Sciences. 95(4). 1663–1668.512 indexed citations breakdown →
Kumamaru, Toshihiro, Hikaru Satoh, Nobuo Iwata, Takeshi Omura, & Masahiro Ogawa. (1987). Mutants for rice storage proteins. III. Genetic analysis of mutants for storage proteins of protein bodies in the starchy endosperm.:III. Genetic analysis of mutants for storage proteins of protein bodies in the starchy endosperm. 62(4). 333–339.9 indexed citations
Iwata, Nobuo & Takeshi Omura. (1984). Studies on the trisomics in rice plants (Oryza sativa L.). VI. An accomplishment of a trisomic series in japonica rice plants.:VI. An accomplishment of a trisomic series in japonica rice plants. Genes & Genetic Systems. 59(3). 199–204.10 indexed citations
Iwata, Nobuo & Takeshi Omura. (1976). STUDIES ON THE TRISOMICS IN RICE PLANTS ( ORYZA SATIVA L.):IV. ON THE POSSIBILITY OF ASSOCIATION OF THREE LINKAGE GROUPS WITH ONE CHROMOSOME. 51(2). 135–137.20 indexed citations
Iwata, Nobuo, et al.. (1970). Cytogenetical studies on the progenies of rice plants exposed to atomic radiation in Nagasaki. 25(1). 1–53.10 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.