Haruka Hirose

mmVelo estimates cell state-dependent dynamics across multiple modalities using deep generative modeling, validated on mouse and human data.

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.

LineageVAE reconstructs historical cell states and transcriptomes from scRNA-seq data to infer unobserved progenitor cell states.

ExDyn is a deep generative model that estimates single-cell transcriptome dynamics influenced by extrinsic factors, showing improved accuracy over existing methods.

DeepCOLOR identifies cell-cell interactions by reconstructing spatial colocalization maps from single-cell and spatial transcriptomes in various tissues.

The paper investigates the tumor-suppressive role of c-MAF in colorectal cancer by analyzing its expression and effects in mouse models and human samples.