LegNet: a best-in-class deep learning model for short DNA regulatory regions.

ABSTRACT:

Motivation

The increasing volume of data from high-throughput experiments including parallel reporter assays facilitates the development of complex deep-learning approaches for modeling DNA regulatory grammar.

Results

Here, we introduce LegNet, an EfficientNetV2-inspired convolutional network for modeling short gene regulatory regions. By approaching the sequence-to-expression regression problem as a soft classification task, LegNet secured first place for the autosome.org team in the DREAM 2022 challenge of predicting gene expression from gigantic parallel reporter assays. Using published data, here, we demonstrate that LegNet outperforms existing models and accurately predicts gene expression per se as well as the effects of single-nucleotide variants. Furthermore, we show

SUBMITTER: Penzar D 

PROVIDER: S-EPMC10400376 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature