Kengo Sakurai

This study applies genomic selection to improve medicinal compounds in red perilla, showing that GS-based crosses outperform phenotypic selection in target compound levels.

This paper introduces CPS-MT, a breeding strategy that improves a target trait while maintaining essential traits within ranges by evaluating cross-pairs based on predicted progeny distributions.

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

This study proposes a method to select optimal cross pairs in inter-population crosses by considering genetic effects across populations, showing increased genetic gains in multiple traits.

This paper introduces Cross Potential Selection (CPS), a strategy for selecting crossing pairs in plant breeding to accelerate novel variety production, showing higher genetic improvements in early cycles of simulated breeding programs.

This study evaluates genomic selection and marker-assisted selection for improving medicinal compound production in red perilla using quantitative trait locus analysis and genomic prediction.

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