Shuto Hayashi

CloneVI: A deep generative model for deciphering the relationships between cell state and mitochondrial genotype

CloneVI infers clonal structure from cell states in single-cell RNA-seq and ATAC-seq data, linking mitochondrial DNA mutations to specific cell states.

Closed-Loop Multi-Objective Optimization for Receptor-Selective Cell-Penetrating Peptide Design

The paper develops a closed-loop framework for designing receptor-selective cell-penetrating peptides using multi-objective optimization and evaluates its effectiveness through experimental validation.

mmVelo: A deep generative model for estimating cell state-dependent dynamics across multiple modalities

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

Probabilistic coupling of cellular and microenvironmental heterogeneity by masked self-supervised learning

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.

scSurv: a deep generative model for single-cell survival analysis

scSurv estimates individual cellular contributions to cancer patient survival using a deep generative model combined with Cox proportional hazards analysis.

A deep generative model for estimating single-cell RNA splicing and degradation rates

DeepKINET estimates single-cell RNA splicing and degradation rates from scRNA-seq data, outperforming existing methods and revealing cellular heterogeneity in post-transcriptional regulation.

Unsupervised deep learning enables blur-free super-resolution in two-photon microscopy

This paper introduces an unsupervised deep learning method for deblurring, super-resolution, and segmentation of two-photon microscopy images, achieving better performance than conventional methods.

Inferring extrinsic factor-dependent single-cell transcriptome dynamics using a deep generative model

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

Single-cell colocalization analysis using a deep generative model

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

A multifunctional R package for identification of tumor-specific neoantigens

The paper introduces an R package for predicting tumor-specific neoantigens from various mutations and integrating additional biological information.

Quantifying Immune-Based Counterselection of Somatic Mutations

The paper quantifies immune-based counterselection of somatic mutations by analyzing their depletion in MHC class I-displayed peptides and shows that MHC alleles are incompletely dominant.

Immuno-genomic Pan-cancer Landscape Reveals Diverse Immune Escape Mechanisms and Immuno-Editing Histories

The paper analyzes immuno-genomic data across 31 cancer types to identify immune escape mechanisms and immuno-editing histories.

ALPHLARD: a Bayesian method for analyzing HLA genes from whole genome sequence data

ALPHLARD is a Bayesian method for HLA genotyping from WGS data, achieving high accuracy and detecting rare variants and somatic mutations.