Advancing mRNA subcellular localization prediction with graph neural network and RNA structure.

ABSTRACT:

Motivation

The asymmetrical distribution of expressed mRNAs tightly controls the precise synthesis of proteins within human cells. This non-uniform distribution, a cornerstone of developmental biology, plays a pivotal role in numerous cellular processes. To advance our comprehension of gene regulatory networks, it is essential to develop computational tools for accurately identifying the subcellular localizations of mRNAs. However, considering multi-localization phenomena remains limited in existing approaches, with none considering the influence of RNA's secondary structure.

Results

In this study, we propose Allocator, a multi-view parallel deep learning framework that seamlessly integrates the RNA sequence-level and structure-level information, enhancing the prediction of

SUBMITTER: Li F 

PROVIDER: S-EPMC11361792 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature