Publications

Journal Publications

(1) Yoshioka H, Mary-Huard T, Aubert J, Toda Y, Ohmori Y, Yamasaki Y, Tsujimoto H, Takahashi H, Nakazono M, Takanashi H, Fujiwara T, Tsuda M, Kaga A, Fuji Y, Hirai MY, Nose Y, Kumaishi K, Usui E, Kobori S, Sato T, Narukawa M, Ichihashi Y, and Iwata H (2026). Integration of Proxy Intermediate Omics Traits in a Nonlinear Two-Step Model for Accurate Phenotype Prediction. Theor Appl Genet 139, 86. doi:  https://doi.org/10.1007/s00122-026-05171-3 

(2) Yoshioka H, Morota G, and Iwata H (2025). Reciprocal BLUP: A Predictability-Guided Multi-Omics Framework for Plant Phenotype Prediction. Plants 2026, 15(1), 17 doi: 10.3390/plants15010017

(3) Yoshioka H, Aubert J, and Mary-Huard T (2025). rrda: Ridge Redundancy Analysis for High-Dimensional Omics Data. doi:10.32614/CRAN.package.rrda (CRAN R Package) 

(4) Yoshioka H, Kimura K, Ogo Y, Ohtsuki N, Nishizawa-Yokoi A, Itoh H, Toki S, and Izawa T (2021)  Real-Time Monitoring of Key Gene Products Involved in Rice Photoperiodic Flowering. Front.  Plant Sci. 12:766450. doi: 10.3389/fpls.2021.766450 

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(5) Yoshioka H, Debeljak P, Prado S, and Iwata H (2025). Interpretable Multi-Omics Machine Learning Reveals Drought-Driven Shifts in Plant-Microbe Interactions. doi: https://doi.org/10.1101/2025.08.13.670005 (bioRxiv)

(6) Yoshioka H, Aubert J, Iwata H, and Mary-Huard T (2025). Ridge Redundancy Analysis for High-Dimensional Omics Data. doi:10.1101/2025.04.16.649138 (bioRxiv) 

Presentations

(1) Yoshioka H, Mary-Huard T, Aubert J, and Iwata H (2024) “A Machine Learning Approach to Improve Yield Prediction Model with Multi-omics Data" The 55th Journées de Statistique de la SFdS, France.

(2) Yoshioka H and Iwata H (2022) “Modeling of plant-microbe interactions using multi-omics data" The 142nd Japanese Society of Breeding Meeting, Japan.

(3) Yoshioka H and Izawa T. (2022) “Development of in vivo monitoring system for proteins regulating photoperiodic flowering with Deep Learning.” Selection Symposium, John Innes Centre, UK. 

(4) Yoshioka H and Izawa T. (2022) “Development of in vivo monitoring system for proteins regulating photoperiodic flowering with Deep Learning.” Selection Symposium, Max Planck MPIPZ, Germany.