Demo Data from EDBT 2026 / reviewers in the wild / expert
Natsumaro Kutsuna
dblp:209/8141
· DBLP profile ↗
2ranked-venue papers
0as first author
0since 2021 · last 2017
0000-0001-8554-2578ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 2
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Bioinformatics and computational biology · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Bioinformatics and computational biology
transcriptomics |
0.3 | 1 | 2017 | FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions · Bioinform. 2017 |
Bioinformatics and computational biology
parameter optimization |
0.1 | 1 | 2017 | FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditions · Bioinform. 2017 |
Methods — techniques the papers use, named apart from their topics
statistical modeling · 0.3parameter optimization · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | FIT: statistical modeling tool for transcriptome dynamics under fluctuating field conditionsabstractMOTIVATION: Considerable attention has been given to the quantification of environmental effects on organisms. In natural conditions, environmental factors are continuously changing in a complex manner. To reveal the effects of such environmental variations on organisms, transcriptome data in field environments have been collected and analyzed. Nagano et al. proposed a model that describes the relationship between transcriptomic variation and environmental conditions and demonstrated the capability to predict transcriptome variation in rice plants. However, the computational cost of parameter optimization has prevented its wide application. RESULTS: : We propose a new statistical model and efficient parameter optimization based on the previous study. We developed and released FIT, an R package that offers functions for parameter optimization and transcriptome prediction. The proposed method achieves comparable or better prediction performance within a shorter computational time than the previous method. The package will facilitate the study of the environmental effects on transcriptomic variation in field conditions. AVAILABILITY AND IMPLEMENTATION: Freely available from CRAN ( https://cran.r-project.org/web/packages/FIT/ ). CONTACT: : [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Koji Iwayama, Yuri Aisaka, Natsumaro Kutsuna, Atsushi J. Nagano |
Bioinform. | 3 |
| 2016 | A Theoretical Model of Jigsaw-Puzzle Pattern Formation by Plant Leaf Epidermal CellsabstractPlant leaf epidermal cells exhibit a jigsaw puzzle-like pattern that is generated by interdigitation of the cell wall during leaf development. The contribution of two ROP GTPases, ROP2 and ROP6, to the cytoskeletal dynamics that regulate epidermal cell wall interdigitation has already been examined; however, how interactions between these molecules result in pattern formation remains to be elucidated. Here, we propose a simple interface equation model that incorporates both the cell wall remodeling activity of ROP GTPases and the diffusible signaling molecules by which they are regulated. This model successfully reproduces pattern formation observed in vivo, and explains the counterintuitive experimental results of decreased cellulose production and increased thickness. Our model also reproduces the dynamics of three-way cell wall junctions. Therefore, this model provides a possible mechanism for cell wall interdigitation formation in vivo. Takumi Higaki, Natsumaro Kutsuna, Kae Akita, Hisako Takigawa-Imamura, Kenji Yoshimura, Takashi Miura |
PLoS Comput. Biol. | 2 |