MRSLpred-a hybrid approach for predicting multi-label subcellular localization of mRNA at the genome scale.

ABSTRACT: In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using m

SUBMITTER: Choudhury S 

PROVIDER: S-EPMC10877048 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature