CRMnet: A deep learning model for predicting gene expression from large regulatory sequence datasets.

ABSTRACT: Recent large datasets measuring the gene expression of millions of possible gene promoter sequences provide a resource to design and train optimized deep neural network architectures to predict expression from sequences. High predictive performance due to the modeling of dependencies within and between regulatory sequences is an enabler for biological discoveries in gene regulation through model interpretation techniques. To understand the regulatory code that delineates gene expression, we have designed a novel deep-learning model (CRMnet) to predict gene expression in Saccharomyces cerevisiae. Our model outperforms the current benchmark models and achieves a Pearson correlation coefficient of 0.971 and a mean squared error of 3.200. Interpretation of informative genomic regions de

SUBMITTER: Ding K 

PROVIDER: S-EPMC10043243 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature