A review of machine learning-based prediction of lncRNA subcellular localization

With the continuous development of the field of bioinformatics, the subcellular localization of long non-coding RNA (lncRNA) has become a highly prominent frontier. LncRNAs play crucial regulatory roles in cellular processes, and understanding their subcellular localization is essential for comprehending their functions and mechanisms. However, traditional experimental methods face challenges of high costs and time consumption when predicting the subcellular localization of lncRNAs on a large scale, which has led to the emergence of research methods based on machine learning. This review aims to recap the latest advancements and trends in machine learning-based prediction of lncRNA subcellular localization in recent years. It not only provides new opportunities for a better understanding of lncRNA functions and cellular processes but also propels advancements in the fields of bioinformatics and molecular biology.