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Ryuei Nishii

Showing results (1-10 of 8) with videos related to

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Journal of Applied Statistics|June 16, 2022
Minimizing the expected value of the asymmetric loss function and an inequality for the variance of the lossNaoya Yamaguchi, Yuka Yamaguchi, Ryuei Nishii
Plant & Cell Physiology|May 12, 2020
Decoding Plant-Environment Interactions That Influence Crop Agronomic TraitsKeiichi Mochida, Ryuei Nishii, Takashi Hirayama
Frontiers in Plant Science|December 18, 2018
Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome DatasetsKeiichi Mochida, Satoru Koda, Komaki Inoue, et al.
Gigascience|December 7, 2018
Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspectiveKeiichi Mochida, Satoru Koda, Komaki Inoue, et al.
Iscience|May 27, 2020
Genetic Factors Associated with Heading Responses Revealed by Field Evaluation of 274 Barley Accessions for 20 SeasonsKazuhiro Sato, Makoto Ishii, Kotaro Takahagi, et al.
Frontiers in Plant Science|December 14, 2017
Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in <i>Brachypodium distachyon</i>Satoru Koda, Yoshihiko Onda, Hidetoshi Matsui, et al.
NAR Genomics and Bioinformatics|February 12, 2021
Parental legacy and regulatory novelty in <i>Brachypodium</i> diurnal transcriptomes accompanying their polyploidyKomaki Inoue, Kotaro Takahagi, Yusuke Kouzai, et al.
Journal of Genetics and Genomics = Yi Chuan Xue Bao|December 24, 2022
Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landracesJune-Sik Kim, Kotaro Takahagi, Komaki Inoue, et al.
Pageof 1

Showing results (1-10 of 8) with videos related to

Sort By:
Pageof 1
Journal of Applied Statistics|June 16, 2022
Minimizing the expected value of the asymmetric loss function and an inequality for the variance of the lossNaoya Yamaguchi, Yuka Yamaguchi, Ryuei Nishii
Plant & Cell Physiology|May 12, 2020
Decoding Plant-Environment Interactions That Influence Crop Agronomic TraitsKeiichi Mochida, Ryuei Nishii, Takashi Hirayama
Frontiers in Plant Science|December 18, 2018
Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome DatasetsKeiichi Mochida, Satoru Koda, Komaki Inoue, et al.
Gigascience|December 7, 2018
Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspectiveKeiichi Mochida, Satoru Koda, Komaki Inoue, et al.
Iscience|May 27, 2020
Genetic Factors Associated with Heading Responses Revealed by Field Evaluation of 274 Barley Accessions for 20 SeasonsKazuhiro Sato, Makoto Ishii, Kotaro Takahagi, et al.
Frontiers in Plant Science|December 14, 2017
Diurnal Transcriptome and Gene Network Represented through Sparse Modeling in <i>Brachypodium distachyon</i>Satoru Koda, Yoshihiko Onda, Hidetoshi Matsui, et al.
NAR Genomics and Bioinformatics|February 12, 2021
Parental legacy and regulatory novelty in <i>Brachypodium</i> diurnal transcriptomes accompanying their polyploidyKomaki Inoue, Kotaro Takahagi, Yusuke Kouzai, et al.
Journal of Genetics and Genomics = Yi Chuan Xue Bao|December 24, 2022
Exome-wide variation in a diverse barley panel reveals genetic associations with ten agronomic traits in Eastern landracesJune-Sik Kim, Kotaro Takahagi, Komaki Inoue, et al.
Pageof 1