Rui Yamaguchi

SPATNIC is a variational autoencoder that identifies cancer cells from spatial and single-cell transcriptomic data in colorectal cancer, outperforming existing tools and identifying a rare cancer stem cell population.

The paper introduces INHERIT, a deep learning model that combines database-based and alignment-free methods for phage identification, achieving an F1-score of 0.9932 on a benchmark dataset.

The paper investigates the association between amylase gene copy numbers and diabetes, obesity, and gut microbiome function in a northern Japanese population, finding a positive link between higher amylase copy numbers and these conditions.

This paper explores using refined BERT models for nanopore methylation detection, achieving competitive performance and faster inference speed compared to biRNN models.

RDBKE improves breakpoint resolution of read-depth SV callers using a deep segmentation model, increasing the number of SVs with more precise breakpoints on simulation and real data.

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

This paper introduces a base-calling method for nanopore sequencing by framing it as a one-dimensional segmentation task, achieving competitive accuracy compared to existing methods.

This study uses supervised machine learning to classify BCR/Ig sequences from normal and tumor tissues, finding distinct sequence-level features between the two environments.

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

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