EDCLoc: a prediction model for mRNA subcellular localization using improved focal loss to address multi-label class imbalance.

ABSTRACT:

Background

The subcellular localization of mRNA plays a crucial role in gene expression regulation and various cellular processes. However, existing wet lab techniques like RNA-FISH are usually time-consuming, labor-intensive, and limited to specific tissue types. Researchers have developed several computational methods to predict mRNA subcellular localization to address this. These methods face the problem of class imbalance in multi-label classification, causing models to favor majority classes and overlook minority classes during training. Additionally, traditional feature extraction methods have high computational costs, incomplete features, and may lead to the loss of critical information. On the other hand, deep learning methods face challenges related to hardware performance

SUBMITTER: Deng Y 

PROVIDER: S-EPMC11674359 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature