A self-attention model for inferring cooperativity between regulatory features.

ABSTRACT: Deep learning has demonstrated its predictive power in modeling complex biological phenomena such as gene expression. The value of these models hinges not only on their accuracy, but also on the ability to extract biologically relevant information from the trained models. While there has been much recent work on developing feature attribution methods that discover the most important features for a given sequence, inferring cooperativity between regulatory elements, which is the hallmark of phenomena such as gene expression, remains an open problem. We present SATORI, a Self-ATtentiOn based model to detect Regulatory element Interactions. Our approach combines convolutional layers with a self-attention mechanism that helps us capture a global view of the landscape of interactions between re

SUBMITTER: Ullah F 

PROVIDER: S-EPMC8287919 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature