Zirui Fan

The paper maps phenotypic and genetic links between plasma proteins and organ imaging traits using UK Biobank data, identifying associations and causal links across multiple organs.

The paper identifies rare protein-coding variants associated with multi-organ MRI traits using exome-wide association analysis in the UK Biobank.

This study uses Mendelian randomization to identify genetic causal links between 58 diseases, 56 imaging traits, and multiple organs across 402 imaging traits and 372 clinical outcomes.

DynaFold generates protein dynamic trajectories using a latent diffusion model, achieving high accuracy and efficiency in predicting conformational ensembles and transitions.

StruCloze reconstructs atomistic structures from coarse-grained models and inpaints missing regions in biomolecules, achieving state-of-the-art accuracy and transferability.

The paper investigates organ-specific and modality-specific aging clocks using proteomic and imaging data, revealing distinct biological processes and their roles in age-related diseases.

PennPRS provides a cloud platform for training polygenic risk scores without individual-level data, validated with simulations and real data across 6,000 phenotypes.

The paper introduces BIGA, a cloud platform for analyzing cross-trait genetic architecture using GWAS summary statistics, enabling unified data analysis and resource sharing.

The paper uses genetic data to predict imaging-derived phenotypes from the UK Biobank, creating polygenic scores that identify disease risk in external cohorts.

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 study identifies genetic overlaps between sleep traits and multi-organ imaging biomarkers, showing strong genetic links between sleep and psychiatric disorders.

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.