A Pretraining-Retraining Strategy of Deep Learning Improves Cell-Specific Enhancer Predictions.

ABSTRACT: Deciphering the code of cis-regulatory element (CRE) is one of the core issues of today's biology. Enhancers are distal CREs and play significant roles in gene transcriptional regulation. Although identifications of enhancer locations across the whole genome [discriminative enhancer predictions (DEP)] is necessary, it is more important to predict in which specific cell or tissue types, they will be activated and functional [tissue-specific enhancer predictions (TSEP)]. Although existing deep learning models achieved great successes in DEP, they cannot be directly employed in TSEP because a specific cell or tissue type only has a limited number of available enhancer samples for training. Here, we first adopted a reported deep learning architecture and then developed a novel training strateg

SUBMITTER: Niu X 

PROVIDER: S-EPMC6960260 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature