Yusuke Toda

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.

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

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

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

This study analyzes panicle temperature trends in Japan over 45 years to assess rice heat exposure and its implications for breeding and crop management.

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

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