Shinya Tasaki

This study uses multimodal transcriptomic data to enhance Alzheimer's disease risk gene discovery, identifying 161 risk genes with proteomic support and an interconnected network of hub genes.

The paper demonstrates the feasibility of detecting methylation differences in serum-derived cell-free DNA between Alzheimer's disease and non-AD controls using LABS, linking these changes to brain-specific epigenetic alterations in AD.

This study integrates multi-omics data from DLPFC with GWAS summary data to identify PD risk genes, finding significant overlaps between bulk and cell-type-aware TWAS and PWAS results.

This study evaluates deep learning models for gene expression prediction in disease contexts, finding they perform poorly in disease-relevant applications despite high overall correlation.

The paper analyzes microglial communication in Alzheimer's disease using single-nucleus RNA sequencing to identify altered signaling pathways and cell interactions associated with disease pathology.

This study integrates multi-omics data from aged human brains to identify cross-system biological factors and molecular subtypes associated with Alzheimer's disease heterogeneity.

The paper develops a model to predict gene relevance in behavioral research using a large language model, revealing shared gene signatures between molecular and behavioral studies.

The paper identifies cell type-specific gene modules associated with Alzheimer's Disease traits using snRNASeq data from human brain tissues.

This study identifies molecular predictors of neuronal dysfunction in Alzheimer's disease by analyzing E/I imbalance and its relationship with neuropathology.

This study examines cytokine gene expression in human brain tissue from elderly individuals with and without Alzheimer's disease to identify biomarkers and understand disease mechanisms.

This paper develops an APOE-independent polygenic risk score for Alzheimer's disease that shows associations with cognitive decline and neuropathological markers across diverse populations.

The paper introduces int2MR, a Mendelian randomization method for detecting exposure-by-group interactions using GWAS summary statistics, showing its effectiveness in identifying sex- and age-group-specific risk factors for ADHD and Alzheimer's disease.

The paper defines cognitive resilience as differential cognitive decline rates after adjusting for neuropathologies, develops a framework to quantify it using molecular signatures from post-mortem brain data, and translates these findings to living individuals.

This paper introduces a computational pipeline for genotyping TOMM40 poly-T polymorphisms from whole-genome sequencing data, achieving high accuracy and consistency with PCR-based genotypes.

The paper uses spatial transcriptomics to analyze Aβ plaques and glial interactions in AD brains, finding that low Aβ areas show more neuronal loss and high-glia areas show inflammation and neurodegeneration.

The paper uses an omnibus xWAS pipeline to integrate snRNA-seq and GWAS data, identifying 91 independent cell-type-specific risk genes for Alzheimer's disease dementia.

The paper identifies three brain multi-omic molecular subtypes of neuroticism linked to Alzheimer's disease traits, with subtype N1 showing the strongest associations.

This study translates post-mortem brain multi-omics AD dementia subtypes into blood, MRI, and psychological markers in living individuals.

The paper investigates structural variants (SVs) in AD/ADRD traits using whole genome sequencing data from two cohort studies, identifying SVs linked to disease phenotypes and suggesting new risk loci.

The paper identifies AD-associated lncRNAs with spatial and cell-type-specific expression patterns and shows OIP5-AS1 contributes to microglial activation in AD.

This study identifies YTHDF proteins as key regulators of Alzheimer's disease brain gene signatures through m6A-dependent and independent mechanisms.

The paper computes polygenic risk scores for various disorders and correlates them with T cell gene expression in Alzheimer's patients and controls, identifying gene pathways related to T cell signaling and activation.

This study benchmarks deep neural networks for predicting gene expression from DNA sequence across individuals and identifies limitations in predicting variant effects.

This study identifies 928 genes associated with brain weight variation across three datasets and links them to neurodevelopmental processes and brain function.

This paper introduces PentaMind, a deep learning model that extracts cognitive features from hand-drawn intersecting pentagons, improving detection of cognitive decline in older adults.

The paper identifies proteins and molecular processes related to brain connectivity by integrating neuroimaging, proteomic, and gene expression data from human brain regions.

SR-TWAS improves TWAS power by combining multiple reference panels and regression methods through ensemble learning, identifying risk genes for Alzheimer's and Parkinson's diseases.

The paper introduces PWAS-O, an omnibus proteome-wide association study method that identifies 43 risk genes for Alzheimer's disease dementia, including 5 novel ones.

CEWAS improves gene detection from GWAS by modeling cascading epigenomic and transcriptomic effects, identifying disease-related genes and novel biological processes.