Evaluation and optimization of sequence-based gene regulatory deep learning models.

ABSTRACT: Neural networks have emerged as immensely powerful tools in predicting functional genomic regions, notably evidenced by recent successes in deciphering gene regulatory logic. However, a systematic evaluation of how model architectures and training strategies impact genomics model performance is lacking. To address this gap, we held a DREAM Challenge where competitors trained models on a dataset of millions of random promoter DNA sequences and corresponding expression levels, experimentally determined in yeast, to best capture the relationship between regulatory DNA and gene expression. For a robust evaluation of the models, we designed a comprehensive suite of benchmarks encompassing various sequence types. While some benchmarks produced similar results across the top-performing models, ot

SUBMITTER: Rafi AM 

PROVIDER: S-EPMC10888977 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature