Xingzhong Zhao

The paper introduces sum-h2, a fast framework for evaluating genetic discovery from deep learning-derived phenotypes using heritability measures, and establishes a Genetic Discovery Arena for benchmarking.

TorchGWAS2 improves genome-wide association testing in related samples by using a deterministic variance-correction algorithm, achieving faster and more powerful results across large-scale omics data.

PerturbLDM predicts single-cell transcriptional responses to perturbations using a latent diffusion model, outperforming existing methods in drug-dose-cell line combinations and capturing key biological programs.

This study evaluates the replicability of unsupervised deep learning image phenotypes across different models, data splits, and cohorts, finding significant stability and biological relevance.

The paper uses deep learning dynamics to uncover epoch-specific genetic architectures of brain morphology from UK Biobank data.

This paper introduces a multimodal contrastive learning framework to derive heritable brain representations from paired T1- and T2-weighted MRIs, improving genetic discovery and revealing shared biological targets.

This study uses deep learning to derive image phenotypes from cardiac MRI and performs GWAS, identifying 323 SNPs and 628 genes associated with cardiac traits and diseases.

The paper improves brain age estimation using an adversarial convolution network and identifies BAG-associated genes and regulatory networks linked to brain disorders.

This paper introduces a deep learning framework to analyze fractional anisotropy images, identifying genetic variants associated with white matter microstructure and linking them to neurological disorders.

The paper identifies CD4+CXCR5+PD-1- Tfh cells as prognostic and predictive biomarkers for R-CHOP therapy response in DLBCL using single-cell RNA sequencing and multiplex IHC.

This study uses Mendelian randomization to identify cell type-specific causal genes linking brain imaging phenotypes and disorders, revealing high specificity and potential therapeutic targets.

This study identifies age-specific gene signatures in the human cerebral cortex using TCA, linking them to structural features and neuropsychiatric disorders.