Interpreting cis-regulatory interactions from large-scale deep neural networks.

ABSTRACT: The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN predictions with orthogonal experimental data, providing insights into generalization but offering limited insights into their decision-making process. Existing model explainability tools focus mainly on motif analysis, which becomes complex when interpreting longer sequences. Here we present cis-regulatory element model explanations (CREME), an in silico perturbation toolkit that interprets the rules of gene regulation learned by a genomic DNN. Applying CREME to Enformer, a state-of-the-art DNN, we identify cis-regulatory elements that enhance or silence gene expression and characterize their comp

SUBMITTER: Toneyan S 

PROVIDER: S-EPMC12065635 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature