A review of computational model-based prediction of lncRNA subcellular localization

Long non-coding RNAs (lncRNA) play pivotal roles in diverse cellular processes, and the determination of lncRNA subcellular localization serves as crucial information for elucidating their functional roles. However, conventional biochemical experimental methodologies employed for identifying lncRNA subcellular localization exhibit inherent complexities, challenges in reproducibility, and substantial costs. In the contemporary era of burgeoning bioinformatics, computational models for predicting the subcellular localization of biomolecules offer a viable alternative. Notably, these computational approaches boast high efficiency and relatively lower costs, presenting a substantial reduction in time and human resource expenditure compared to traditional experimental protocols. This comprehensive review encapsulates the latest strides in leveraging computational models for the prediction of lncRNA subcellular localization, offering novel avenues for a profound comprehension of lncRNA functionality and their intricate involvement in cellular processes.