Yusuke Matsui

GlyEnzEA constructs enzyme-specific glycan sets from glycomics data and identifies significant changes in specific enzyme groups.

This paper evaluates when site-specific glycoform compositions can resolve biosynthetic rates, showing that sufficient panel size, separable responses, and model fit are required for accurate inference.

The paper introduces glycan reachability analysis to quantify tissue-specific glycan biosynthetic potential from transcriptomics, revealing quantitative differences missed by binary methods.

The paper develops an enrichment-free glycoproteomics method using mass defect-driven glycopeptide classification to identify sex differences in murine plasma fucosylation.

GlycoMeSH links glycan structures to biomedical contexts through a database and enrichment workflow, improving association recovery and coverage over existing methods.

OmicsLake tracks dataset lineage in R/Bioconductor workflows by associating stored versions with dependencies and agent identifiers.

The paper defines sex-specific glycoproteomes in mouse tissues using multi-omics integration, revealing distinct glycoform patterns and enzyme relationships across 19 tissues.

GlycoTraitR is an R package that provides a unified framework for analyzing structural heterogeneity in N-linked glycoproteomics data through trait-based analysis.

GlycanGT is a foundation model for glycans that uses a graph transformer to improve classification and prediction of glycan structures and properties.

This study identifies exercise-induced anti-aging processes in skeletal muscle by analyzing intron retention and its link to genomic instability and mitochondrial function.

This study uses deep learning to integrate proteomics and PPI data, identifying astrocytes and GPNMB+ microglia as novel moderators of Aβ-tau interaction in early Alzheimer's disease.

HuTAge integrates cross-tissue age-related data to provide human tissue- and cell-specific ageing molecular information.

The paper uses machine learning on multi-cohort transcriptomic and proteomic data to identify molecular subtypes of cognitive resilience in Alzheimer's disease and finds two major subgroups with distinct proteomic and neuroimaging features.

This paper introduces a framework to interpret embedding spaces from dimension reduction by mapping biological functions to structural patterns in two datasets.

The paper identifies dysregulation of PLOD3 in oligodendrocytes as an upstream regulator of COL4A5, linked to amyloid fiber formation in Alzheimer's disease.

The paper identifies Oncostatin M as a driver of inflammation in knee osteoarthritis using network-based cytokine inference and pharmacological manipulation in aged mice.

This study identifies Pgc-1α as a regulator of fat accumulation in aged skeletal muscle by analyzing transcriptomic responses to exercise and immobilization.

This study uses meta-analysis and multi-omics to identify inflammation, autophagy, and cellular senescence as key features of age-related KOA in mice, with unique molecular profiles distinct from post-traumatic OA.

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 shows that KDM7A and UTX demethylate repressive histone marks to enable NF-κB-dependent inflammatory gene activation in endothelial cells.

The paper introduces a Network of Networks framework and GOSPEL algorithm to model interconnectivity between brain-specific functional networks and identify autism-associated genetic structures.

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.

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.