Ron Okimoto

1.7k total citations
20 papers, 1.2k citations indexed

About

Ron Okimoto is a scholar working on Animal Science and Zoology, Genetics and Molecular Biology. According to data from OpenAlex, Ron Okimoto has authored 20 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Animal Science and Zoology, 8 papers in Genetics and 6 papers in Molecular Biology. Recurrent topics in Ron Okimoto's work include Animal Nutrition and Physiology (7 papers), Genetic and phenotypic traits in livestock (6 papers) and Genetic Mapping and Diversity in Plants and Animals (5 papers). Ron Okimoto is often cited by papers focused on Animal Nutrition and Physiology (7 papers), Genetic and phenotypic traits in livestock (6 papers) and Genetic Mapping and Diversity in Plants and Animals (5 papers). Ron Okimoto collaborates with scholars based in United States, Netherlands and China. Ron Okimoto's co-authors include William M. Muir, Hans H. Cheng, Martien A. M. Groenen, Addie Vereijken, R.P.M.A. Crooijmans, Ben Dorshorst, Christopher M. Ashwell, Hendrik‐Jan Megens, Rachel Hawken and Sonchita Bagchi and has published in prestigious journals such as Proceedings of the National Academy of Sciences, PLoS ONE and Genetics.

In The Last Decade

Ron Okimoto

20 papers receiving 1.2k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ron Okimoto United States 13 637 427 353 152 147 20 1.2k
C. A. Gill United States 18 891 1.4× 406 1.0× 234 0.7× 97 0.6× 207 1.4× 66 1.4k
Inger Edfors‐Lilja Sweden 21 1.1k 1.7× 500 1.2× 410 1.2× 171 1.1× 293 2.0× 41 1.8k
Lujiang Qu China 27 1.1k 1.7× 1.1k 2.5× 649 1.8× 124 0.8× 155 1.1× 129 2.3k
Alexandre Rodrigues Caetano Brazil 22 883 1.4× 145 0.3× 427 1.2× 74 0.5× 217 1.5× 101 1.4k
Addie Vereijken Netherlands 26 1.6k 2.5× 1.3k 3.1× 415 1.2× 123 0.8× 419 2.9× 58 2.5k
Yuanmei Guo China 25 1.1k 1.7× 327 0.8× 399 1.1× 56 0.4× 186 1.3× 70 1.5k
Elisabeth Jonas Sweden 20 718 1.1× 269 0.6× 379 1.1× 98 0.6× 215 1.5× 47 1.3k
Hubert H. Levéziel France 27 974 1.5× 467 1.1× 574 1.6× 319 2.1× 153 1.0× 79 1.8k
Valentina Riggio United Kingdom 20 845 1.3× 262 0.6× 218 0.6× 127 0.8× 214 1.5× 59 1.3k
Giuseppina Schiavo Italy 20 852 1.3× 353 0.8× 364 1.0× 72 0.5× 94 0.6× 92 1.3k

Countries citing papers authored by Ron Okimoto

Since Specialization
Citations

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

Fields of papers citing papers by Ron Okimoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ron Okimoto

This figure shows the co-authorship network connecting the top 25 collaborators of Ron Okimoto. A scholar is included among the top collaborators of Ron Okimoto 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 Ron Okimoto. Ron Okimoto 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.
Díaz‐Sánchez, Sandra, Allison Perrotta, Eric J. Alm, et al.. (2019). Using fecal microbiota as biomarkers for predictions of performance in the selective breeding process of pedigree broiler breeders. PLoS ONE. 14(5). e0216080–e0216080. 36 indexed citations
3.
Okimoto, Ron, et al.. (2018). Triploidy in broiler chickens: a brief review and case description. Proceedings of the World Congress on Genetics Applied to Livestock Production. 715. 1 indexed citations
4.
Hudson, Nicholas J., Walter Bottje, Rachel Hawken, et al.. (2017). Mitochondrial metabolism: a driver of energy utilisation and product quality?. Animal Production Science. 57(11). 2204–2215. 12 indexed citations
5.
Hudson, Nicholas J., Rachel Hawken, Ron Okimoto, R. L. Sapp, & Antônio Reverter. (2017). Data compression can discriminate broilers by selection line, detect haplotypes, and estimate genetic potential for complex phenotypes. Poultry Science. 96(9). 3031–3038. 5 indexed citations
6.
Juul‐Madsen, Helle Risdahl, Rikke Brødsgaard Kjærup, Ron Okimoto, et al.. (2016). Broilers with low serum Mannose-binding Lectin show increased fecal shedding of Salmonella enterica serovar Montevideo. Poultry Science. 95(8). 1779–1786. 10 indexed citations
7.
Reverter, Antônio, Ron Okimoto, R. L. Sapp, et al.. (2016). Chicken muscle mitochondrial content appears coordinately regulated and is associated with performance phenotypes. Biology Open. 28 indexed citations
8.
Wang, Huiyu, I. Misztal, Ignácio Aguilar, et al.. (2014). Genome-wide association mapping including phenotypes from relatives without genotypes in a single-step (ssGWAS) for 6-week body weight in broiler chickens. Frontiers in Genetics. 5. 134–134. 178 indexed citations
9.
Zhang, Xinyue, I. Misztal, J.W.M. Bastiaansen, et al.. (2014). Prior genetic architecture impacting genomic regions under selection: An example using genomic selection in two poultry breeds. Livestock Science. 171. 1–11. 3 indexed citations
11.
Wang, Y., Vinayak Brahmakshatriya, Blanca Lupiani, et al.. (2012). Associations of chicken Mx1 polymorphism with antiviral responses in avian influenza virus infected embryos and broilers. Poultry Science. 91(12). 3019–3024. 19 indexed citations
12.
Wang, Ying, Vinayak Brahmakshatriya, Blanca Lupiani, et al.. (2012). Integrated analysis of microRNA expression and mRNA transcriptome in lungs of avian influenza virus infected broilers. BMC Genomics. 13(1). 278–278. 88 indexed citations
13.
Groenen, Martien A. M., Hendrik‐Jan Megens, Yalda Zare, et al.. (2011). The development and characterization of a 60K SNP chip for chicken. BMC Genomics. 12(1). 274–274. 166 indexed citations
14.
Kerstens, Hindrik H. D., R.P.M.A. Crooijmans, Bert Dibbits, et al.. (2011). Structural variation in the chicken genome identified by paired-end next-generation DNA sequencing of reduced representation libraries. BMC Genomics. 12(1). 94–94. 22 indexed citations
15.
Dorshorst, Ben, Ron Okimoto, & Christopher M. Ashwell. (2010). Genomic Regions Associated with Dermal Hyperpigmentation, Polydactyly and Other Morphological Traits in the Silkie Chicken. Journal of Heredity. 101(3). 339–350. 100 indexed citations
16.
Muir, William M., Gane Ka‐Shu Wong, Yong Zhang, et al.. (2008). Review of the initial validation and characterization of a 3K chicken SNP array. World s Poultry Science Journal. 64(2). 219–226. 29 indexed citations
17.
Muir, William M., Gane Ka‐Shu Wong, Yong Zhang, et al.. (2008). Genome-wide assessment of worldwide chicken SNP genetic diversity indicates significant absence of rare alleles in commercial breeds. Proceedings of the National Academy of Sciences. 105(45). 17312–17317. 220 indexed citations
18.
Bacon, L D, Mohammad Heidari, William M. Muir, et al.. (2007). Genetic variation at the tumour virus B locus in commercial and laboratory chicken populations assessed by a medium-throughput or a high-throughput assay. Avian Pathology. 36(4). 283–291. 11 indexed citations
20.
Georges, Michel, Dahlia M. Nielsen, Margaret J. Mackinnon, et al.. (1995). Mapping quantitative trait loci controlling milk production by exploiting progeny testing. Open Repository and Bibliography (University of Liège). 29 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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