Yasunori Ichihashi

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 identifies chromid-like secondary replicons in Ktedonobacteria as key genomic regions harboring biosynthetic gene clusters, expanding understanding of soil microbial secondary metabolism.

This study shows that horizontal gene transfer of nicotine catabolism genes in Arthrobacter enables its colonization of tobacco roots, shaping the root microbiota community.

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.

This study introduces a new genomic DNA library preparation method that reduces GC bias and improves coverage breadth compared to existing protocols.

The paper introduces a new tomato population for high-resolution QTL mapping and gene identification, demonstrating its utility for fine-mapping and epistatic QTL analysis.

This study identifies 7,200 eQTL regulating transcript levels of 5,300 genes in tomato, revealing complex genetic architecture and associations with biological processes and phenotypes.

The paper identifies auxin signaling and cell wall expansion genes as key factors in natural variation of shade avoidance in tomato, linking them to growth plasticity and QTL effects on stem elongation and developmental rate.

This study shows that shade avoidance in tomato plants alters leaf shape through changes in gene expression related to indeterminacy, not through juvenile leaf type prolongation.