Computational prediction of disease related lncRNAs using machine learning

Long non-coding RNAs (lncRNAs), which were once considered as transcriptional noise, are now in the limelight of current research. LncRNAs play a major role in regulating various biological processes such as imprinting, cell differentiation, and splicing. The mutations of lncRNAs are involved in various complex diseases. Identifying lncRNA-disease associations has gained a lot of attention as predicting it efficiently will lead towards better disease treatment. In this study, we have developed a machine learning model that predicts disease-related lncRNAs by combining sequence and structure-based features. The features were trained on SVM and Random Forest classifiers. We have compared our method with the state-of-the-art and obtained the highest F1 score of 76% on SVM classifier. Moreover, this study has overcome two serious limitations of the reported method which are lack of redundancy checking and implementation of oversampling for balancing the positive and negative class. Our method has achieved improved performance among machine learning models reported for lncRNA-disease associations. Combining multiple features together specifically lncRNAs sequence mutation has a significant contribution to the disease related lncRNA prediction.

Automated summary

Long non-coding RNAs (lncRNAs) have emerged as important players in regulating biological processes and their mutations are associated with complex diseases. In this study, we developed a machine learning model that combines sequence and structure-based features to predict disease-related lncRNAs. Our model outperformed existing methods and achieved the highest F1 score of 76% on the SVM classifier. We also addressed limitations in previous methods by incorporating redundancy checking and oversampling techniques. Combining multiple features, particularly lncRNA sequence mutations, significantly contributed to the prediction of disease-related lncRNAs.