Jingwen Yan

This paper develops a continuous amyloid staging score from regional amyloid PET to improve early Alzheimer's disease staging and cognitive risk stratification.

This paper models Alzheimer's disease progression as a deviation from aging using longitudinal amyloid PET data and a self-supervised trajectory-learning framework, revealing distinct trajectories in early-stage aging and AD.

The paper analyzes proteomic trajectories in Alzheimer's disease to show distinct temporal progression of shared molecular programs across brain regions.

MINTsC identifies multi-way chromatin interactions from scHi-C data, revealing gene regulation by multiple enhancers and reducing multiple-testing burden in molecular QTL studies.

This study introduces SLOPE, a method to model amyloid progression in Alzheimer's disease on a continuous trajectory, showing improved sensitivity to early changes compared to global measures.

This study investigates how Alzheimer's risk SNPs affect chromatin functions and how their genomic context influences these effects.

The paper introduces MoFNet, a deep neural network that integrates multi-omic data to identify functional subnetworks associated with Alzheimer's Disease, showing replication in an independent cohort.

This study identifies genetic influences on brain connectivity across age groups using GWAS and finds a consistent effect of rs7937515 on network segregation in the left middle temporal gyrus.

The paper identifies a functionally connected subnetwork of 276 multi-omic biomarkers for Alzheimer’s Disease using a modularity-constrained Lasso model applied to the ROS/MAP cohort.

The paper integrates genomic and metabolomic data with biological networks to identify genetic variants and bile acids associated with Alzheimer's disease, revealing functional biological paths across multiple-omics layers.

The paper identifies differences in bile acid pathways for cholesterol clearance in Alzheimer’s disease by analyzing transcriptomes and metabolomics data from brain samples.