Dmytro Chebotarov

3.7k total citations
25 papers, 961 citations indexed

About

Dmytro Chebotarov is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Dmytro Chebotarov has authored 25 papers receiving a total of 961 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Plant Science, 14 papers in Genetics and 13 papers in Molecular Biology. Recurrent topics in Dmytro Chebotarov's work include Genetic Mapping and Diversity in Plants and Animals (14 papers), Rice Cultivation and Yield Improvement (11 papers) and Genomics and Phylogenetic Studies (11 papers). Dmytro Chebotarov is often cited by papers focused on Genetic Mapping and Diversity in Plants and Animals (14 papers), Rice Cultivation and Yield Improvement (11 papers) and Genomics and Phylogenetic Studies (11 papers). Dmytro Chebotarov collaborates with scholars based in Philippines, United States and China. Dmytro Chebotarov's co-authors include Millicent D. Alexandrov Sanciangco, Ramil Mauleon, Nickolai Alexandrov, Roven Rommel Fuentes, Locedie Mansueto, Ruaraidh Sackville Hamilton, Rod A. Wing, Zhikang Li, Gengyun Zhang and Wensheng Wang and has published in prestigious journals such as Nucleic Acids Research, Nature Communications and PLANT PHYSIOLOGY.

In The Last Decade

Dmytro Chebotarov

24 papers receiving 949 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dmytro Chebotarov Philippines 12 818 493 331 21 19 25 961
Hisataka Numa Japan 10 636 0.8× 274 0.6× 405 1.2× 18 0.9× 12 0.6× 23 802
Roven Rommel Fuentes Netherlands 8 611 0.7× 378 0.8× 267 0.8× 18 0.9× 8 0.4× 9 702
Sharon Westcott Australia 15 617 0.8× 282 0.6× 177 0.5× 24 1.1× 24 1.3× 29 748
Lianjun Sun China 14 878 1.1× 235 0.5× 283 0.9× 20 1.0× 15 0.8× 26 1.0k
Eddi Esteban Canada 13 546 0.7× 144 0.3× 303 0.9× 18 0.9× 12 0.6× 20 651
Karl A. Kremling United States 12 561 0.7× 271 0.5× 394 1.2× 26 1.2× 16 0.8× 18 771
Makiko Chono Japan 16 910 1.1× 135 0.3× 413 1.2× 34 1.6× 27 1.4× 26 1.0k
Dangping Luo United States 8 1.1k 1.3× 178 0.4× 580 1.8× 42 2.0× 39 2.1× 9 1.3k
Tuan Long China 9 437 0.5× 142 0.3× 321 1.0× 21 1.0× 20 1.1× 12 535
Naoya Urasaki Japan 13 473 0.6× 177 0.4× 332 1.0× 24 1.1× 22 1.2× 33 598

Countries citing papers authored by Dmytro Chebotarov

Since Specialization
Citations

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

Fields of papers citing papers by Dmytro Chebotarov

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dmytro Chebotarov

This figure shows the co-authorship network connecting the top 25 collaborators of Dmytro Chebotarov. A scholar is included among the top collaborators of Dmytro Chebotarov 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 Dmytro Chebotarov. Dmytro Chebotarov 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
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2.
Egdane, James, Dmytro Chebotarov, Kazuyuki Doi, et al.. (2024). Leaf Na+ effects and multi-trait GWAS point to salt exclusion as the key mechanism for reproductive stage salinity tolerance in rice. Annals of Botany. 135(5). 949–962.
3.
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Zhou, Yong, Nagarajan Kathiresan, Zhichao Yu, et al.. (2024). A high-performance computational workflow to accelerate GATK SNP detection across a 25-genome dataset. BMC Biology. 22(1). 13–13. 4 indexed citations
5.
Henry, Amelia, et al.. (2024). Genotypic screening for salinity tolerance of rice genotypes from Eastern and Southern Africa at seedling stage. Journal of Plant Interactions. 19(1). 2 indexed citations
6.
Chebotarov, Dmytro, Jianwei Zhang, David Kudrna, et al.. (2023). Oryza glumaepatula: A wild relative to improve drought tolerance in cultivated rice. PLANT PHYSIOLOGY. 193(4). 2381–2397. 4 indexed citations
7.
Liao, Qiong, Dmytro Chebotarov, Marjorie De Ocampo, et al.. (2022). Aus rice root architecture variation contributing to grain yield under drought suggests a key role of nodal root diameter class. Plant Cell & Environment. 45(3). 854–870. 19 indexed citations
8.
Siangliw, Jonaliza L., Dmytro Chebotarov, Millicent D. Alexandrov Sanciangco, et al.. (2022). Response of Southeast Asian rice root architecture and anatomy phenotypes to drought stress. Frontiers in Plant Science. 13. 1008954–1008954. 5 indexed citations
9.
Chebotarov, Dmytro, Ranjita Thapa, John Carlos I. Ignacio, et al.. (2021). Enriched-GWAS and Transcriptome Analysis to Refine and Characterize a Major QTL for Anaerobic Germination Tolerance in Rice. International Journal of Molecular Sciences. 22(9). 4445–4445. 11 indexed citations
10.
Chebotarov, Dmytro, Millicent D. Alexandrov Sanciangco, Valerien O. Pede, et al.. (2021). Novel Sources of Pre-Harvest Sprouting Resistance for Japonica Rice Improvement. Plants. 10(8). 1709–1709. 14 indexed citations
11.
Zhou, Yong, Dmytro Chebotarov, Dave Kudrna, et al.. (2020). A platinum standard pan-genome resource that represents the population structure of Asian rice. Scientific Data. 7(1). 83 indexed citations
12.
Mauleon, Ramil, Dmytro Chebotarov, Ajay Kohli, et al.. (2020). Mass genome sequencing of crops and wild relatives to accelerate crop breeding: the digital rice genebank. IOP Conference Series Earth and Environmental Science. 482(1). 12005–12005. 2 indexed citations
13.
Chebotarov, Dmytro, et al.. (2020). Advanced Strategic Research to Promote the Use of Rice Genetic Resources. Agronomy. 10(11). 1629–1629. 8 indexed citations
14.
Fuentes, Roven Rommel, Dmytro Chebotarov, Jorge Duitama, et al.. (2019). Structural variants in 3000 rice genomes. Genome Research. 29(5). 870–880. 99 indexed citations
15.
Wang, Diane, Francisco Agosto-Perez, Dmytro Chebotarov, et al.. (2018). An imputation platform to enhance integration of rice genetic resources. Nature Communications. 9(1). 3519–3519. 56 indexed citations
16.
Onaga, Geoffrey, et al.. (2017). High temperature effects on Pi54 conferred resistance to Magnaporthe oryzae in two genetic backgrounds of Oryza sativa. Journal of Plant Physiology. 212. 80–93. 25 indexed citations
17.
Mansueto, Locedie, Roven Rommel Fuentes, Frances Nikki Borja, et al.. (2016). Rice SNP-seek database update: new SNPs, indels, and queries. Nucleic Acids Research. 45(D1). D1075–D1081. 228 indexed citations
18.
Tatarinova, Tatiana V., Yuri Nikolsky, Sergey Bruskin, et al.. (2016). Nucleotide diversity analysis highlights functionally important genomic regions. Scientific Reports. 6(1). 35730–35730. 41 indexed citations
19.
Mansueto, Locedie, Roven Rommel Fuentes, Dmytro Chebotarov, et al.. (2016). SNP-Seek II: A resource for allele mining and analysis of big genomic data in Oryza sativa. Current Plant Biology. 7-8. 16–25. 32 indexed citations
20.
Alexandrov, Nickolai, Shuaishuai Tai, Wensheng Wang, et al.. (2014). SNP-Seek database of SNPs derived from 3000 rice genomes. Nucleic Acids Research. 43(D1). D1023–D1027. 266 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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