DeepTAD: an approach for identifying topologically associated domains based on convolutional neural network and transformer model.

ABSTRACT:

Motivation

Topologically associated domains (TADs) play a key role in the 3D organization and function of genomes, and accurate detection of TADs is essential for revealing the relationship between genomic structure and function. Most current methods are developed to extract features in Hi-C interaction matrix to identify TADs. However, due to complexities in Hi-C contact matrices, it is difficult to directly extract features associated with TADs, which prevents current methods from identifying accurate TADs.

Results

In this paper, a novel method is proposed, deepTAD, which is developed based on a convolutional neural network (CNN) and transformer model. First, based on Hi-C contact matrix, deepTAD utilizes CNN to directly extract features associated with TAD boundaries. Nex

SUBMITTER: Wang X 

PROVIDER: S-EPMC11934553 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature