mRSubLoc: A novel multi-label learning framework integrating RNA large language model for mRNA subcellular localization

The subcellular localization of messenger RNA (mRNA) is essential for the regulation of gene expression and plays a pivotal role in targeted drug development. Although several computational models have been developed to predict mRNA localization, these approaches still face challenges in sequence representation and exhibit limited performance in handling multi-localization tasks. In this paper, we propose mRSubLoc, a novel multi-label deep learning framework for predicting mRNA subcellular localization. The model integrates the RNA large language model RNAErnie with one-hot encoding and Word2Vec embeddings to construct a comprehensive representation of mRNA sequences. A text convolutional neural network (TextCNN) is employed to capture local feature patterns, while a bidirectional long short-term memory network (BiLSTM) is used to capture long-range dependencies. These features are fused using a multi-head self-attention mechanism to effectively capture localization-specific characteristics. Finally, a multi-layer perceptron (MLP) explores complex dependencies among multiple localization sites, facilitating accurate mRNA subcellular localization prediction. Experimental results on a testing set demonstrate that mRSubLoc significantly outperforms state-of-the-art methods across multiple metrics, including Aiming (0.7858), Coverage (0.6212), Accuracy (0.6161), Absolute-True (0.3070), and Absolute-False (0.1319). This study proposes a novel approach for predicting mRNA subcellular localization and provides new perspectives for advancing disease diagnosis and drug discovery in biomedical research.