The transferability and clinical value of genetic risk scores (GRSs) across populations
remain limited due to an imbalance in genetic studies across ancestrally diverse …

The exponential growth of big data in RNA biology (RB) has led to the development of deep
learning (DL) models that have driven crucial discoveries. As constantly evidenced by DL …

Single-cell omics technologies enable molecular characterization of diverse cell types and
states, but how the resulting transcriptional and epigenetic profiles depend on the cell's …

Enhancer-gene communication is dependent on topologically associating domains (TADs)
and boundaries enforced by the CCCTC-binding factor (CTCF) insulator, but the underlying …

The epigenome—the chemical modifications and chromatin-related packaging of the
genome—enables the same genetic template to be activated or repressed in different …

High-quality genome assemblies and sophisticated algorithms have increased sensitivity for
a wide range of variant types, and breakpoint accuracy for structural variants (SVs,≥ 50 bp) …

Epigenetic modifications are dynamic mechanisms involved in the regulation of gene
expression. Unlike the DNA sequence, epigenetic patterns vary not only between …

Single-cell genomics is a powerful tool for studying heterogeneous tissues such as the
brain. Yet little is understood about how genetic variants influence cell-level gene …

We propose a new framework for human genetic association studies: at each locus, a deep
learning model (in this study, Sei) is used to calculate the functional genomic activity score …

Non-coding variants associated with complex traits can alter the motifs of transcription factor
(TF)–deoxyribonucleic acid binding. Although many computational models have been …