Hayato Yoshioka

The paper uses interpretable machine learning to analyze multi-omics data from soybean under drought stress, identifying key biomarkers and interactions influencing plant phenotypes.

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.

The paper introduces Reciprocal BLUP, a multi-omics framework that improves plant phenotype prediction by analyzing cross-layer relationships between genome, metabolome, and microbiome data.

This paper introduces an efficient framework for ridge RDA to analyze high-dimensional omics data by addressing computational challenges through SVD and reduced memory storage.