Junhao Wen

This paper links molecular mechanisms to Alzheimer's disease and aging by integrating neuroimaging endophenotypes with molecular quantitative trait loci.

The paper develops and integrates multi-organ proteome-based biological age gaps (ProtBAGs) with phenotype-based age gaps to explore their genetic and clinical implications in aging and disease.

The paper identifies nine neuroimaging endophenotypes linked to genetic loci and shows they improve prediction of systemic diseases and mortality.

The paper develops organ-specific metabolome-based biological age gaps using UK Biobank data and links them to cardiometabolic conditions and mortality risk.

This paper introduces UNITO, a deep learning framework for automated cytometric gating that matches human performance and provides interpretable results without requiring manual input.

This study identifies 393 genomic loci associated with biological age gaps in nine human organ systems using UK Biobank data, revealing organ-specific and pleiotropic genetic effects.

The paper uses GWAS summary statistics to analyze the genetic architecture of 2024 multi-organ AI endophenotypes, revealing genetic correlations and causal relationships across diseases.

This study identifies sixteen genome-wide significant loci associated with brain age gaps derived from MRI modalities and highlights potential causal relationships between brain aging and diseases.

The paper identifies 617 novel genomic loci and pathways associated with structural covariance patterns in the human brain using a stochastic multivariate factorization method.

This paper introduces MAGIC, a multi-scale semi-supervised clustering method for brain imaging data to identify disease subtypes, and evaluates its performance on simulated and clinical datasets.

The paper introduces a reproducible framework for evaluating AD classification methods using MRI and PET data, showing FDG PET outperforms T1 MRI and linear classifiers perform best.