MVLncStack: A novel method for lncRNA subcellular localization prediction based on multi-view features

LncRNA performs different life activities in different subcellulars. For the subcellular multi-classification localization task of lncRNA, this paper proposed an ensemble learning prediction model named MVLncStack based on random forest and deep learning. Random forest in MVLncStack was employed to further analyze the sequence-level features of lncRNA, and selected the optimal feature subset with incremental feature selection. The deep learning model in MVLncStack was employed to learn the residue-level features of lncRNA with position encoding. RNA multi-view features provided the sequence information and structure information of lncRNA for MVLncStack, which can enhance the prediction performance of the model. Compared with the existing methods on the independent test set, the accuracy of multi classification prediction was improved by 5.2%, and other indicators were improved by about 1% - 2%. Through the performance evaluation and related experimental analysis, the importance of multi-view features of RNA was illustrated, and the prediction performance of the models can be effectively improved by selecting appropriate feature coding for RNA.