Evaluating deep learning for predicting epigenomic profiles.

ABSTRACT: Deep learning has been successful at predicting epigenomic profiles from DNA sequences. Most approaches frame this task as a binary classification relying on peak callers to define functional activity. Recently, quantitative models have emerged to directly predict the experimental coverage values as a regression. As new models continue to emerge with different architectures and training configurations, a major bottleneck is forming due to the lack of ability to fairly assess the novelty of proposed models and their utility for downstream biological discovery. Here we introduce a unified evaluation framework and use it to compare various binary and quantitative models trained to predict chromatin accessibility data. We highlight various modeling choices that affect generalization performanc

SUBMITTER: Toneyan S 

PROVIDER: S-EPMC10270674 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature