Tianyou Luo

This paper benchmarks longitudinal local centile maps for predicting radiographic knee osteoarthritis progression using MRI data.

This paper introduces CBSS, a framework for analyzing structural connectomes across multiple cohorts, showing its reliability and utility in cross-cohort diffusion MRI studies.

MAST-Decon improves spatial transcriptomics deconvolution by incorporating spatial neighborhood and histological image features to enhance accuracy and reliability.

This paper introduces launch-dCas9, a machine learning framework to predict guide RNA effects in CRISPR epigenome editing, showing high accuracy and improved gRNA selection.

The paper investigates the genetic impact of the X-chromosome and sex differences in the human brain, identifying 72 genome-wide significant trait-locus pairs linked to brain-related disorders.

POLARIS infers cell composition from histology images and identifies layer-specific differentially expressed genes in spatial transcriptomics data.

The paper identifies 60 genetic loci associated with brain ventricular and subcortical shape features using shape measures from over 45,000 UK Biobank and ABCD study subjects.

This paper compares eight ST deconvolution methods using four real datasets to evaluate their performance in inferring cell type composition from spatial transcriptomics data.

This study identifies common genetic variants influencing intrinsic brain networks using rsfMRI data from 44,190 individuals, finding associations with cognitive and clinical traits.

The paper investigates phenotypic and genetic links between heart and brain using MRI data from 40,000 UK Biobank subjects, finding strong correlations and shared genetic influences.

This study identifies genetic loci associated with functional brain networks and areas using large-scale fMRI data, linking them to cognitive traits and brain disorders.

This study identifies common genetic variants influencing white matter microstructure using dMRI data from 42,919 individuals, revealing genetic links to neuropsychiatric disorders and other traits.

This study performs transcriptome-wide association analysis on 211 neuroimaging traits, identifies 204 genes, and shows TWAS-derived polygenic risk scores explain up to 6.97% of phenotypic variance.

This study identifies 287 novel genetic variants associated with brain volumes using GWAS in 19,629 UK Biobank participants and finds genetic correlations with cognitive and mental health traits.