Finding motifs using DNA images derived from sparse representations.

ABSTRACT:

Motivation

Motifs play a crucial role in computational biology, as they provide valuable information about the binding specificity of proteins. However, conventional motif discovery methods typically rely on simple combinatoric or probabilistic approaches, which can be biased by heuristics such as substring-masking for multiple motif discovery. In recent years, deep neural networks have become increasingly popular for motif discovery, as they are capable of capturing complex patterns in data. Nonetheless, inferring motifs from neural networks remains a challenging problem, both from a modeling and computational standpoint, despite the success of these networks in supervised learning tasks.

Results

We present a principled representation learning approach based on a hierarchic

SUBMITTER: Chu SK 

PROVIDER: S-EPMC10290554 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature