Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants.

ABSTRACT:

Motivation

Genome-wide association studies (GWAS) have identified numerous noncoding variants associated with complex human diseases, disorders, and traits. However, resolving the uncertainty between GWAS association and causality remains a significant challenge. The small subset of noncoding GWAS variants with causative effects on gene regulatory elements can only be detected through accurate methods that assess the impact of DNA sequence variation on gene regulatory activity. Deep learning models, such as those based on Convolutional Neural Networks (CNNs) and transformers, have gained prominence in predicting the regulatory effects of genetic variants, particularly in enhancers, by learning patterns from genomic and epigenomic data. Despite their potential, selecting the most su

SUBMITTER: Manzo G 

PROVIDER: S-EPMC12190767 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature