The paper proposes a nonlinear two-step model integrating proxy intermediate omics traits to improve phenotypic prediction in soybean, showing better accuracy than linear models.

Theoretical and Applied Genetics · Mar 1, 2026Peer-reviewedBioinformatics

I-SVVS: Integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data

Tung Dang, Yushiro Fuji, Kie Kumaishi, Erika Usui, Shungo Kobori, Takumi Sato, Megumi Narukawa, Yusuke Toda, Kengo Sakurai, Yuji Yamasaki, Hisashi Tsujimoto, Masami Yokota Hirai, Yasunori Ichihashi, Hiroyoshi Iwata

I-SVVS improves clustering accuracy and reduces computation time for multi-omics microbiome data across soybean, mice, and human datasets.

Briefings in Bioinformatics · May 1, 2025Peer-reviewedBioinformatics

Modeling soybean growth: A mixed model approach

Maud Delattre, Yusuke Toda, Jessica Tressou, Hiroyoshi Iwata

The paper proposes a nonlinear mixed-effects model to analyze genetic and environmental effects on soybean growth using simulated and experimental data.

PLoS Computational Biology · Jul 11, 2024Peer-reviewedGenetics

High-throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models

Mashiro Okada, Clément Barras, Yusuke Toda, Kosuke Hamazaki, Yoshihiro Ohmori, Yuji Yamasaki, Hirokazu Takahashi, Hideki Takanashi, Mai Tsuda, Masami Yokota Hirai, Hisashi Tsujimoto, Akito Kaga, Mikio Nakazono, Toru Fujiwara, Hiroyoshi Iwata

This study uses UAV remote sensing and deep learning to estimate soybean biomass traits and extract latent features for breeding applications.

Plant Phenomics · Jan 1, 2024Peer-reviewedPlant Biology

Evaluating drought tolerance stability in soybean by theresponse of irrigation change captured from time-seriesmultispectral data

Kengo Sakurai, Yusuke Toda, Kosuke Hamazaki, Yoshihiro Ohmori, Yuji Yamasaki, Hirokazu Takahashi, Hideki Takanashi, Mai Tsuda, Hisashi Tsujimoto, Akito Kaga, Mikio Nakazono, Toru Fujiwara, Hiroyoshi Iwata

This study evaluates drought tolerance stability in soybean using time-series multispectral data and genomic prediction models, finding higher accuracy with changing irrigation treatments.

bioRxiv · Apr 5, 2023 · PreprintGenetics

Stochastic variational variable selection for high-dimensional microbiome data

Tung Dang, Kie Kumaishi, Erika Usui, Shungo Kobori, Takumi Sato, Yusuke Toda, Yuji Yamasaki, Hisashi Tsujimoto, Yasunori Ichihashi, Hiroyoshi Iwata

The paper introduces SVVS, a method for analyzing high-dimensional microbiome data, showing improved performance and faster computation compared to existing methods.

Microbiome · Dec 24, 2022Peer-reviewedBioinformatics