Fei Qin

The paper identifies lung cancer susceptibility copy number variants (CNVs) and shows their potential role in genomic instability through association analyses and functional validation.

MintCNA improves CNA detection in single-cell multi-omics data by integrating deep learning and multivariate change-point detection, outperforming existing methods in simulations and showing strong correlation with bulk sequencing.

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

The paper constructs a single-cell eQTL dataset from Asian never-smokers, identifying alveolar cell-specific genes linked to lung cancer susceptibility.

GFBioNet identifies genomic factors modulating trait-trait relationships in multi-omics data, revealing host genetics, microbial taxa, and mutations affecting biological networks.

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 paper introduces SPADE, a method for identifying spatially variable genes in spatial transcriptomic data, showing improved performance over existing methods in both within and between-group analyses.

This paper introduces FLCNA, a statistical method for simultaneous copy number estimation and subclone clustering in scDNA-seq data, showing improved performance over existing methods in breast cancer samples.

This paper introduces LDcnv, a novel algorithm that improves copy number variation detection by integrating genomic correlation structure, showing higher accuracy and precision than existing methods.