An ensemble learning method combined with multiple feature representation strategies to predict lncRNA subcellular localizations

Long non-coding RNAs (lncRNAs) are strongly associated with cellular physiological mechanisms and implicated in the numerous diseases. By exploring the subcellular localizations of lncRNAs, we can not only gain crucial insights into the molecular mechanisms of lncRNA-related biological processes but also make valuable contributions towards the diagnosis, prevention, and treatment of various human diseases. However, conventional experimental techniques tend to be laborious and time-intensive. In this context, computational methods are in increased demand. The focus of this paper is the development of an innovative ensemble method that incorporates hybrid features to accurately predict the subcellular localizations of lncRNAs. To address the issue of incomplete reflection of inherent correlation with the intended target using singular source features, the utilization of heterogeneous multi-source features is implemented by introducing information on sequence composition, physicochemical properties, and structure. To address the issue of the imbalance classes in the benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is employed. Finally, the resulting predictor termed lncSLPre is developed by integrating the outputs of the individual classifiers. Experimental findings suggest that the complementarity of multi-source heterogeneous features improves prediction performance. Additionally, it is demonstrated that the application of SMOTE is effective in mitigating the issue of the imbalanced dataset, while the feature selection approach is critical in eliminating extraneous and redundant features. Compared with existing advanced methods, lncSLPre achieves better performance with an overall accuracy improvement of 13.13%, 2.15%, and 3.23%, respectively, indicating that lncSLPre can effectively predict lncRNA subcellular localizations.