Hiroyuki Mano

PRCGAP provides a haplotype-resolved analysis of cancer genomes, detecting variants missed by standard references and revealing new insights into structural variants and fusion events in leukemia.

Mievformer uses masked self-supervised learning to model microenvironmental and cellular heterogeneity in spatial transcriptomics data, outperforming existing methods in niche clustering and real dataset analysis.

The paper uses high-speed AFM to study how ALK inhibitors affect the structure and dynamics of EML4-ALK variants, revealing IDR-mediated plasticity and resistance mechanisms.

The paper develops a pipeline for multi-sample long-read transcriptome assembly and applies it to breast cancer specimens to identify subtype-specific isoforms and fusion transcripts.