Yuka Kitomi

3.2k total citations · 1 hit paper
23 papers, 2.1k citations indexed

About

Yuka Kitomi is a scholar working on Plant Science, Genetics and Molecular Biology. According to data from OpenAlex, Yuka Kitomi has authored 23 papers receiving a total of 2.1k indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Plant Science, 7 papers in Genetics and 6 papers in Molecular Biology. Recurrent topics in Yuka Kitomi's work include Plant nutrient uptake and metabolism (15 papers), Rice Cultivation and Yield Improvement (13 papers) and Plant Molecular Biology Research (8 papers). Yuka Kitomi is often cited by papers focused on Plant nutrient uptake and metabolism (15 papers), Rice Cultivation and Yield Improvement (13 papers) and Plant Molecular Biology Research (8 papers). Yuka Kitomi collaborates with scholars based in Japan, Colombia and United States. Yuka Kitomi's co-authors include Yusaku Uga, Yoshiaki Inukai, Noriko Kanno, Masahiro Yano, Hinako Takehisa, Jian Wu, Haruhiko Inoue, Kazuhiko Sugimoto, Naho Hara and Toshiyuki Takai and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Genetics and Development.

In The Last Decade

Yuka Kitomi

22 papers receiving 2.1k citations

Hit Papers

Control of root system architecture by DEEPER ROOTING 1 i... 2013 2026 2017 2021 2013 250 500 750 1000

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yuka Kitomi Japan 15 2.0k 435 349 159 96 23 2.1k
Ritsuko Motoyama Japan 9 1.6k 0.8× 441 1.0× 266 0.8× 116 0.7× 61 0.6× 9 1.7k
Noriko Kanno Japan 10 1.5k 0.7× 222 0.5× 322 0.9× 135 0.8× 76 0.8× 24 1.6k
Long‐Xi Yu United States 20 1.1k 0.5× 209 0.5× 410 1.2× 228 1.4× 43 0.4× 48 1.3k
Rodante E. Tabien United States 17 1.6k 0.8× 224 0.5× 908 2.6× 90 0.6× 55 0.6× 44 1.9k
Prakash R. Arelli United States 30 2.3k 1.2× 221 0.5× 181 0.5× 150 0.9× 184 1.9× 71 2.6k
Zenglu Li United States 25 1.7k 0.8× 296 0.7× 204 0.6× 127 0.8× 16 0.2× 110 1.8k
Anne Laperche France 17 905 0.4× 219 0.5× 232 0.7× 266 1.7× 65 0.7× 31 971
Swetlana Friedel Germany 10 1.1k 0.5× 285 0.7× 143 0.4× 94 0.6× 65 0.7× 11 1.2k
Julie King United Kingdom 23 1.8k 0.9× 242 0.6× 537 1.5× 454 2.9× 84 0.9× 59 1.9k
Uttam Kumar India 19 1.4k 0.7× 104 0.2× 464 1.3× 297 1.9× 42 0.4× 79 1.5k

Countries citing papers authored by Yuka Kitomi

Since Specialization
Citations

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

Fields of papers citing papers by Yuka Kitomi

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yuka Kitomi

This figure shows the co-authorship network connecting the top 25 collaborators of Yuka Kitomi. A scholar is included among the top collaborators of Yuka Kitomi 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 Yuka Kitomi. Yuka Kitomi 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.
Kamoshita, Akihiko, et al.. (2024). Evaluation of rice breeding lines containing root QTLs under different water management environments. 18(0). 22–34. 2 indexed citations
3.
Soma, Fumiyuki, Yuka Kitomi, Taiji Kawakatsu, & Yusaku Uga. (2023). Life-Cycle Multiomics of Rice Shoots Reveals Growth Stage–Specific Effects of Drought Stress and Time–Lag Drought Responses. Plant and Cell Physiology. 65(1). 156–168. 6 indexed citations
4.
Kuya, Noriyuki, Ryo Nishijima, Yuka Kitomi, Taiji Kawakatsu, & Yusaku Uga. (2023). Transcriptome profiles of rice roots under simulated microgravity conditions and following gravistimulation. Frontiers in Plant Science. 14. 1193042–1193042. 6 indexed citations
5.
Hanzawa, Eiko, Yuka Kitomi, Yusaku Uga, & Tadashi Sato. (2022). Quantification of Soil-surface Roots in Seedlings and Mature Rice Plants. BIO-PROTOCOL. 12(9). e4409–e4409. 1 indexed citations
6.
Kawakatsu, Taiji, Shota Teramoto, Satoko Takayasu, et al.. (2021). The transcriptomic landscapes of rice cultivars with diverse root system architectures grown in upland field conditions. The Plant Journal. 106(4). 1177–1190. 17 indexed citations
7.
Tanaka, Tsuyoshi, Ryo Nishijima, Shota Teramoto, et al.. (2020). De novoGenome Assembly of theindicaRice Variety IR64 Using Linked-Read Sequencing and Nanopore Sequencing. G3 Genes Genomes Genetics. 10(5). 1495–1501. 23 indexed citations
8.
Teramoto, Shota, Satoko Takayasu, Yuka Kitomi, et al.. (2020). High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography. Plant Methods. 16(1). 66–66. 85 indexed citations
9.
Kitomi, Yuka, Eiko Hanzawa, Noriyuki Kuya, et al.. (2020). Root angle modifications by the DRO1 homolog improve rice yields in saline paddy fields. Proceedings of the National Academy of Sciences. 117(35). 21242–21250. 162 indexed citations
10.
Teramoto, Shota, et al.. (2019). Backhoe-assisted monolith method for plant root phenotyping under upland conditions. Breeding Science. 69(3). 508–513. 17 indexed citations
11.
Uga, Yusaku, Yuka Kitomi, Brandon Larson, et al.. (2018). Genomic regions responsible for seminal and crown root lengths identified by 2D & 3D root system image analysis. BMC Genomics. 19(1). 273–273. 11 indexed citations
12.
Kitomi, Yuka, Sawako Kawai, Noriko Kanno, et al.. (2017). Fine Mapping of QUICK ROOTING 1 and 2, Quantitative Trait Loci Increasing Root Length in Rice. G3 Genes Genomes Genetics. 8(2). 727–735. 24 indexed citations
13.
Hibara, Ken‐ichiro, Naoki Sentoku, Mikiko Kojima, et al.. (2016). Jasmonate regulates juvenile-adult phase transition in rice. Development. 143(18). 3407–16. 57 indexed citations
14.
Uga, Yusaku, Yuka Kitomi, Eiji Yamamoto, et al.. (2015). A QTL for root growth angle on rice chromosome 7 is involved in the genetic pathway of DEEPER ROOTING 1. Rice. 8(1). 8–8. 67 indexed citations
15.
Kitomi, Yuka, Noriko Kanno, Sawako Kawai, et al.. (2015). QTLs underlying natural variation of root growth angle among rice cultivars with the same functional allele of DEEPER ROOTING 1. Rice. 8(1). 16–16. 70 indexed citations
16.
Uga, Yusaku, Kazuhiko Sugimoto, Satoshi Ogawa, et al.. (2013). Control of root system architecture by DEEPER ROOTING 1 increases rice yield under drought conditions. Nature Genetics. 45(9). 1097–1102. 1078 indexed citations breakdown →
17.
Kitomi, Yuka, et al.. (2012). OsIAA13-mediated auxin signaling is involved in lateral root initiation in rice. Plant Science. 190. 116–122. 107 indexed citations
18.
Kitomi, Yuka & Yoshiaki Inukai. (2011). Molecular mechanisms of crown root initiation in rice. 20(2). 61–71. 1 indexed citations
20.
Kitomi, Yuka, et al.. (2011). Molecular mechanism of crown root initiation and the different mechanisms between crown root and radicle in rice. Plant Signaling & Behavior. 6(9). 1276–1278. 19 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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