Shungo Kobori

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