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

ABSTRACT:

Background/objective

Genome-wide association studies (GWAS) have linked many noncoding variants to complex traits and diseases, but distinguishing as-sociation from causation remains difficult. Deep learning models-particularly CNN- and Transformer-based architectures-are widely used for this task, yet comparisons are hindered by inconsistent benchmarks and evaluation practices. We aimed to establish a standardized assessment of leading models for predicting variant effects in enhancers and for prioritizing putative causal SNPs.

Methods

We evaluated state-of-the-art deep learning models under consistent training and evaluation conditions on nine datasets derived from MPRA, raQTL, and eQTL ex-periments. These datasets profile the regulatory impact of 54,859 single-nucleotide

SUBMITTER: Manzo G 

PROVIDER: S-EPMC12562713 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature