Satoru Miyano

This study identifies 16 AML subgroups based on chromatin accessibility profiles using ATAC-seq in 1,563 cases, revealing epigenetic heterogeneity and its clinical and therapeutic implications.

This study shows that mutations in Asxl1 and Stag2 cause dysregulation of histone modifications and chromatin structures, leading to aberrant gene upregulation and myelodysplastic syndromes.

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.

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.

RoDiCE identifies cancer-specific protein complex aberrations by analyzing differential co-expression with a copula-based method, improving accuracy over linear correlation approaches in proteomic data.

The paper analyzes whole-genome sequencing data from cancer samples to identify genomic footprints of telomere maintenance mechanisms, finding specific telomere variant repeat patterns associated with ATRX/DAXX mutations.

The paper examines how somatic structural variations affect TAD boundaries and gene regulation in human cancers using whole genome sequencing data.

This study analyzes chromothripsis in 2,658 cancers using whole-genome sequencing, finding it prevalent across multiple cancer types with complex structural alterations.

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.

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

This paper introduces MASSIVE, a novel sensitivity analysis method for agent-based simulations using parallel computation and visualization, applied to cancer evolution and identifying heterogeneous tumor conditions.

GIMLET identifies biological modulators in gene regulation by capturing multivariate local dependencies using energy statistics, showing improved performance over existing methods.

This study develops a method to model tumor subclonal progression by linking genetic aberrations to cancer hallmark acquisition using multiregional sequencing data.

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

The paper identifies and characterizes splicing-associated variants in cancer using a statistical framework applied to 8,976 samples across 31 cancer types, revealing 14,438 SAVs and their association with smoking and tumor suppressors.

The paper introduces paplot, a software tool for generating interactive reports in cancer genome analysis, enabling richer data exploration than static visualizations.

The paper describes the PCAWG project's large-scale analysis of cancer whole genomes across multiple computing environments, producing validated consensus variants for downstream research.

The paper introduces phyC, a clustering method for cancer sub-clonal evolutionary trees, and shows it detects clusters related to cancer type or phenotype in real data.

D3M detects differential methylation distributions between case and control groups using Wasserstein metric, showing improved accuracy over existing methods in glioblastoma and lower grade glioma data.

This paper introduces a simplified model-based approach for inferring and visualizing cancer mutation signatures, showing improved robustness and interpretability.

The paper uses a computational model to identify principles of intratumor heterogeneity in cancer evolution.