MIRACN: a residual convolutional neural network for predicting cell line specific functional regulatory variants.

ABSTRACT: In post-genome-wide association study era, interpretation of noncoding variants remains a significant challenge due to their complexity and the limited understanding of their functions. Here, we developed MIRACN, a novel residual convolutional neural network designed to predict cell line-specific functional regulatory variants. By utilizing a substantial dataset from massively parallel reporter assays (MPRAs) and employing a multitask learning strategy, MIRACN was trained across seven distinct cell lines, attaining superior performance compared to existing methods, especially in predicting cell type specificity. Comparative evaluations on an independent MPRA test dataset demonstrated that MIRACN not only outperformed in identifying regulatory variants but also provided valuable insights in

SUBMITTER: Li Z 

PROVIDER: S-EPMC12021264 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature