S. Kinoshita

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 develops a framework combining genomic prediction and Bayesian optimization to efficiently identify high-performing genotype pairs for intercropping.

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