Messenger RNA Subcellular Localization Prediction via Large Language Models and Attention Mechanisms

Messenger RNA (mRNA) exerts its specific functions at distinct subcellular locations, making the localization of mRNA within cells critically important for studying gene expression and cell migration, and also possessing significant medical value. Current mRNA subcellular localization prediction tasks rely on time-consuming and labor-intensive wet lab experiments. Traditional machine learning approaches suffer from weak feature representation capabilities, leading to poor predictive performance. To address this, we proposed LM-mRNALoc, a mRNA subcellular localization prediction model based on large language models and attention mechanisms. This represents the first application of large language models in this field, harnessing their strong representational capabilities to overcome the limitations of previous machine learning approaches. Experiments have shown that compared with state-of-the-art predictors, LM-mRNALoc achieves an F1-score that exceeds the best existing predictors by 12.5 % on independent validation sets. The datasets and codes for LM-mRNALoc are available at https://anonymous.4open.science/r/LM_mRNALoc-28F0.