Abstract
The subcellular localization of long non-coding RNA (lncRNA) provides important insights and opportunities for an in-depth understanding of cell biology, revealing disease mechanisms, drug development, and innovation in the biomedical field. Although several computational methods have been proposed to identify the subcellular localization of lncRNA, it is difficult to accurately predict the subcellular localization of lncRNA effectively with these methods. In this study, a new deep-learning predictor called PreSubLncR has been proposed for accurately predicting the subcellular localization of lncRNA. This predictor firstly used the word embedding model word2vec to encode the RNA sequences, and then combined multi-scale one-dimensional convolutional neural networks with attention and bidirectional long short-term memory networks to capture the different characteristics of various RNA sequences. This study used multiple RNA subcellular localization datasets for experimental validation, and the results showed that our method has higher accuracy and robustness compared with other state-of-the-art methods. It is expected to provide more in-depth insights into cell function research.