IEnhancer-DCSA: identifying enhancers via dual-scale convolution and spatial attention.

ABSTRACT:

Background

Due to the dynamic nature of enhancers, identifying enhancers and their strength are major bioinformatics challenges. With the development of deep learning, several models have facilitated enhancers detection in recent years. However, existing studies either neglect different length motifs information or treat the features at all spatial locations equally. How to effectively use multi-scale motifs information while ignoring irrelevant information is a question worthy of serious consideration. In this paper, we propose an accurate and stable predictor iEnhancer-DCSA, mainly composed of dual-scale fusion and spatial attention, automatically extracting features of different length motifs and selectively focusing on the important features.

Results

Our experimental res

SUBMITTER: Wang W 

PROVIDER: S-EPMC10339552 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature