Qing Lu

CN-RNN is a deep learning framework for detecting copy number variations from exome sequencing data, showing improved performance over existing methods.

BayesCNet improves inference of cell type-specific regulatory networks by leveraging cell type hierarchy, outperforming existing methods, especially for rare cell types.

HapCNV improves CNV detection in low-input haploid sequencing data by using a pseudo-reference to reduce bias and enhance accuracy, especially for short CNVs.

This study shows human APOE variants reduce both diffuse and fibrillar Aβ deposits in transgenic mouse models compared to mouse Apoe, with APOE3 more effective than APOE4 in some contexts.

The paper analyzes rare variants to uncover mutation rate heterogeneity in the human genome, identifying genomic features that influence mutation rates.